Overview

Brought to you by YData

Dataset statistics

Number of variables223
Number of observations455212
Missing cells71318310
Missing cells (%)70.3%
Total size in memory774.5 MiB
Average record size in memory1.7 KiB

Variable types

Numeric32
Unsupported85
Text100
Boolean6

Dataset

DescriptionFish NMNH Extant Specimen Records
CreatorBen Norton
AuthorBen Norton
URLhttps://doi.org/10.15468/dl.34mb2x

Alerts

license has constant value "CC0_1_0" Constant
publisher has constant value "National Museum of Natural History, Smithsonian Institution" Constant
institutionID has constant value "urn:lsid:biocol.org:col:34871" Constant
collectionID has constant value "urn:uuid:09c9cf5f-f5d3-48cc-b5c8-cd9b9fbd631f" Constant
institutionCode has constant value "USNM" Constant
collectionCode has constant value "FISH" Constant
datasetName has constant value "NMNH Extant Biology" Constant
sex has constant value "MALE" Constant
eventID has constant value "941.0" Constant
minimumDistanceAboveSurfaceInMeters has constant value "Williams, Jeffrey T." Constant
earliestEraOrLowestErathem has constant value "Animalia" Constant
latestEraOrHighestErathem has constant value "Chordata" Constant
verbatimIdentification has constant value "SPECIES" Constant
identifiedByID has constant value "ACCEPTED" Constant
identificationVerificationStatus has constant value "821cc27a-e3bb-4bc5-ac34-89ada245069d" Constant
identificationRemarks has constant value "US" Constant
taxonConceptID has constant value "StillImage" Constant
acceptedNameUsage has constant value "False" Constant
nameAccordingTo has constant value "1.0" Constant
namePublishedIn has constant value "44.0" Constant
subtribe has constant value "EML" Constant
nomenclaturalStatus has constant value "PHL.36.21_1" Constant
taxonRemarks has constant value "Iloilo City" Constant
protocol has constant value "EML" Constant
lastCrawled has constant value "2024-12-02T11:48:23.416Z" Constant
publishedByGbifRegion has constant value "NORTH_AMERICA" Constant
hasGeospatialIssues is highly imbalanced (89.7%) Imbalance
isSequenced is highly imbalanced (98.9%) Imbalance
accessRights has 455212 (100.0%) missing values Missing
bibliographicCitation has 455212 (100.0%) missing values Missing
language has 455212 (100.0%) missing values Missing
references has 455212 (100.0%) missing values Missing
rightsHolder has 455212 (100.0%) missing values Missing
type has 455212 (100.0%) missing values Missing
datasetID has 455212 (100.0%) missing values Missing
ownerInstitutionCode has 455212 (100.0%) missing values Missing
informationWithheld has 455212 (100.0%) missing values Missing
dataGeneralizations has 455212 (100.0%) missing values Missing
dynamicProperties has 455212 (100.0%) missing values Missing
recordNumber has 434386 (95.4%) missing values Missing
recordedBy has 287312 (63.1%) missing values Missing
recordedByID has 455212 (100.0%) missing values Missing
organismQuantity has 455212 (100.0%) missing values Missing
organismQuantityType has 455212 (100.0%) missing values Missing
sex has 455209 (> 99.9%) missing values Missing
lifeStage has 455212 (100.0%) missing values Missing
reproductiveCondition has 455212 (100.0%) missing values Missing
caste has 455212 (100.0%) missing values Missing
behavior has 455212 (100.0%) missing values Missing
vitality has 455212 (100.0%) missing values Missing
establishmentMeans has 455212 (100.0%) missing values Missing
degreeOfEstablishment has 455212 (100.0%) missing values Missing
pathway has 455212 (100.0%) missing values Missing
georeferenceVerificationStatus has 455212 (100.0%) missing values Missing
preparations has 346184 (76.0%) missing values Missing
disposition has 455212 (100.0%) missing values Missing
associatedOccurrences has 455212 (100.0%) missing values Missing
associatedReferences has 455212 (100.0%) missing values Missing
associatedSequences has 454762 (99.9%) missing values Missing
associatedTaxa has 455212 (100.0%) missing values Missing
otherCatalogNumbers has 455212 (100.0%) missing values Missing
occurrenceRemarks has 290485 (63.8%) missing values Missing
organismID has 455212 (100.0%) missing values Missing
organismName has 455212 (100.0%) missing values Missing
organismScope has 455212 (100.0%) missing values Missing
associatedOrganisms has 455212 (100.0%) missing values Missing
previousIdentifications has 455212 (100.0%) missing values Missing
organismRemarks has 455212 (100.0%) missing values Missing
materialEntityID has 455212 (100.0%) missing values Missing
materialEntityRemarks has 455212 (100.0%) missing values Missing
verbatimLabel has 455209 (> 99.9%) missing values Missing
materialSampleID has 455209 (> 99.9%) missing values Missing
eventID has 455211 (> 99.9%) missing values Missing
parentEventID has 455212 (100.0%) missing values Missing
eventType has 455212 (100.0%) missing values Missing
fieldNumber has 274211 (60.2%) missing values Missing
eventDate has 60241 (13.2%) missing values Missing
eventTime has 455212 (100.0%) missing values Missing
startDayOfYear has 91500 (20.1%) missing values Missing
endDayOfYear has 91500 (20.1%) missing values Missing
year has 60500 (13.3%) missing values Missing
month has 82757 (18.2%) missing values Missing
day has 108703 (23.9%) missing values Missing
verbatimEventDate has 92472 (20.3%) missing values Missing
habitat has 455212 (100.0%) missing values Missing
samplingProtocol has 455212 (100.0%) missing values Missing
sampleSizeValue has 455212 (100.0%) missing values Missing
sampleSizeUnit has 455212 (100.0%) missing values Missing
samplingEffort has 455212 (100.0%) missing values Missing
fieldNotes has 455212 (100.0%) missing values Missing
eventRemarks has 455212 (100.0%) missing values Missing
locationID has 352012 (77.3%) missing values Missing
higherGeographyID has 455212 (100.0%) missing values Missing
higherGeography has 20492 (4.5%) missing values Missing
continent has 162647 (35.7%) missing values Missing
waterBody has 133275 (29.3%) missing values Missing
islandGroup has 390811 (85.9%) missing values Missing
island has 270596 (59.4%) missing values Missing
countryCode has 30434 (6.7%) missing values Missing
stateProvince has 174301 (38.3%) missing values Missing
county has 357533 (78.5%) missing values Missing
municipality has 455212 (100.0%) missing values Missing
locality has 45084 (9.9%) missing values Missing
verbatimLocality has 455212 (100.0%) missing values Missing
verbatimElevation has 453008 (99.5%) missing values Missing
verticalDatum has 455212 (100.0%) missing values Missing
verbatimDepth has 446636 (98.1%) missing values Missing
minimumDistanceAboveSurfaceInMeters has 455211 (> 99.9%) missing values Missing
maximumDistanceAboveSurfaceInMeters has 455212 (100.0%) missing values Missing
locationAccordingTo has 455212 (100.0%) missing values Missing
locationRemarks has 455212 (100.0%) missing values Missing
decimalLatitude has 254257 (55.9%) missing values Missing
decimalLongitude has 254257 (55.9%) missing values Missing
coordinateUncertaintyInMeters has 450059 (98.9%) missing values Missing
coordinatePrecision has 455212 (100.0%) missing values Missing
pointRadiusSpatialFit has 455205 (> 99.9%) missing values Missing
verbatimCoordinateSystem has 308939 (67.9%) missing values Missing
verbatimSRS has 455212 (100.0%) missing values Missing
footprintWKT has 455212 (100.0%) missing values Missing
footprintSRS has 455212 (100.0%) missing values Missing
footprintSpatialFit has 455212 (100.0%) missing values Missing
georeferencedBy has 455205 (> 99.9%) missing values Missing
georeferencedDate has 455212 (100.0%) missing values Missing
georeferenceProtocol has 437832 (96.2%) missing values Missing
georeferenceSources has 455212 (100.0%) missing values Missing
georeferenceRemarks has 432197 (94.9%) missing values Missing
geologicalContextID has 455212 (100.0%) missing values Missing
earliestEonOrLowestEonothem has 455212 (100.0%) missing values Missing
latestEonOrHighestEonothem has 455205 (> 99.9%) missing values Missing
earliestEraOrLowestErathem has 455205 (> 99.9%) missing values Missing
latestEraOrHighestErathem has 455205 (> 99.9%) missing values Missing
earliestPeriodOrLowestSystem has 455212 (100.0%) missing values Missing
latestPeriodOrHighestSystem has 455205 (> 99.9%) missing values Missing
earliestEpochOrLowestSeries has 455212 (100.0%) missing values Missing
latestEpochOrHighestSeries has 455205 (> 99.9%) missing values Missing
earliestAgeOrLowestStage has 455212 (100.0%) missing values Missing
latestAgeOrHighestStage has 455212 (100.0%) missing values Missing
lowestBiostratigraphicZone has 455212 (100.0%) missing values Missing
highestBiostratigraphicZone has 455205 (> 99.9%) missing values Missing
lithostratigraphicTerms has 455205 (> 99.9%) missing values Missing
group has 455212 (100.0%) missing values Missing
formation has 455212 (100.0%) missing values Missing
member has 455205 (> 99.9%) missing values Missing
bed has 455212 (100.0%) missing values Missing
identificationID has 455212 (100.0%) missing values Missing
verbatimIdentification has 455205 (> 99.9%) missing values Missing
identificationQualifier has 453516 (99.6%) missing values Missing
typeStatus has 436448 (95.9%) missing values Missing
identifiedBy has 421073 (92.5%) missing values Missing
identifiedByID has 455205 (> 99.9%) missing values Missing
dateIdentified has 455212 (100.0%) missing values Missing
identificationReferences has 455212 (100.0%) missing values Missing
identificationVerificationStatus has 455205 (> 99.9%) missing values Missing
identificationRemarks has 455205 (> 99.9%) missing values Missing
taxonID has 455205 (> 99.9%) missing values Missing
scientificNameID has 455212 (100.0%) missing values Missing
parentNameUsageID has 455209 (> 99.9%) missing values Missing
originalNameUsageID has 455209 (> 99.9%) missing values Missing
nameAccordingToID has 455212 (100.0%) missing values Missing
namePublishedInID has 455205 (> 99.9%) missing values Missing
taxonConceptID has 455210 (> 99.9%) missing values Missing
acceptedNameUsage has 455205 (> 99.9%) missing values Missing
parentNameUsage has 455205 (> 99.9%) missing values Missing
originalNameUsage has 455205 (> 99.9%) missing values Missing
nameAccordingTo has 455205 (> 99.9%) missing values Missing
namePublishedIn has 455205 (> 99.9%) missing values Missing
namePublishedInYear has 455212 (100.0%) missing values Missing
class has 444746 (97.7%) missing values Missing
superfamily has 455205 (> 99.9%) missing values Missing
subfamily has 455205 (> 99.9%) missing values Missing
tribe has 455212 (100.0%) missing values Missing
subtribe has 455205 (> 99.9%) missing values Missing
genus has 23586 (5.2%) missing values Missing
genericName has 23579 (5.2%) missing values Missing
subgenus has 455206 (> 99.9%) missing values Missing
infragenericEpithet has 455212 (100.0%) missing values Missing
specificEpithet has 70259 (15.4%) missing values Missing
infraspecificEpithet has 447018 (98.2%) missing values Missing
cultivarEpithet has 455206 (> 99.9%) missing values Missing
verbatimTaxonRank has 455210 (> 99.9%) missing values Missing
vernacularName has 455210 (> 99.9%) missing values Missing
nomenclaturalCode has 455210 (> 99.9%) missing values Missing
nomenclaturalStatus has 455211 (> 99.9%) missing values Missing
taxonRemarks has 455211 (> 99.9%) missing values Missing
elevation has 455212 (100.0%) missing values Missing
elevationAccuracy has 455212 (100.0%) missing values Missing
depth has 246174 (54.1%) missing values Missing
depthAccuracy has 266866 (58.6%) missing values Missing
distanceFromCentroidInMeters has 454306 (99.8%) missing values Missing
mediaType has 363819 (79.9%) missing values Missing
classKey has 444746 (97.7%) missing values Missing
genusKey has 23593 (5.2%) missing values Missing
subgenusKey has 455212 (100.0%) missing values Missing
speciesKey has 70260 (15.4%) missing values Missing
species has 70260 (15.4%) missing values Missing
typifiedName has 455212 (100.0%) missing values Missing
repatriated has 30397 (6.7%) missing values Missing
relativeOrganismQuantity has 455212 (100.0%) missing values Missing
projectId has 455212 (100.0%) missing values Missing
gbifRegion has 32195 (7.1%) missing values Missing
level0Gid has 407295 (89.5%) missing values Missing
level0Name has 407295 (89.5%) missing values Missing
level1Gid has 408402 (89.7%) missing values Missing
level1Name has 408402 (89.7%) missing values Missing
level2Gid has 412023 (90.5%) missing values Missing
level2Name has 412026 (90.5%) missing values Missing
level3Gid has 441377 (97.0%) missing values Missing
level3Name has 441442 (97.0%) missing values Missing
iucnRedListCategory has 11501 (2.5%) missing values Missing
individualCount is highly skewed (γ1 = 51.8804012) Skewed
kingdomKey is highly skewed (γ1 = -46.86227586) Skewed
phylumKey is highly skewed (γ1 = 254.9235179) Skewed
gbifID has unique values Unique
occurrenceID has unique values Unique
accessRights is an unsupported type, check if it needs cleaning or further analysis Unsupported
bibliographicCitation is an unsupported type, check if it needs cleaning or further analysis Unsupported
language is an unsupported type, check if it needs cleaning or further analysis Unsupported
references is an unsupported type, check if it needs cleaning or further analysis Unsupported
rightsHolder is an unsupported type, check if it needs cleaning or further analysis Unsupported
type is an unsupported type, check if it needs cleaning or further analysis Unsupported
datasetID is an unsupported type, check if it needs cleaning or further analysis Unsupported
ownerInstitutionCode is an unsupported type, check if it needs cleaning or further analysis Unsupported
informationWithheld is an unsupported type, check if it needs cleaning or further analysis Unsupported
dataGeneralizations is an unsupported type, check if it needs cleaning or further analysis Unsupported
dynamicProperties is an unsupported type, check if it needs cleaning or further analysis Unsupported
recordedByID is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismQuantity is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismQuantityType is an unsupported type, check if it needs cleaning or further analysis Unsupported
lifeStage is an unsupported type, check if it needs cleaning or further analysis Unsupported
reproductiveCondition is an unsupported type, check if it needs cleaning or further analysis Unsupported
caste is an unsupported type, check if it needs cleaning or further analysis Unsupported
behavior is an unsupported type, check if it needs cleaning or further analysis Unsupported
vitality is an unsupported type, check if it needs cleaning or further analysis Unsupported
establishmentMeans is an unsupported type, check if it needs cleaning or further analysis Unsupported
degreeOfEstablishment is an unsupported type, check if it needs cleaning or further analysis Unsupported
pathway is an unsupported type, check if it needs cleaning or further analysis Unsupported
georeferenceVerificationStatus is an unsupported type, check if it needs cleaning or further analysis Unsupported
disposition is an unsupported type, check if it needs cleaning or further analysis Unsupported
associatedOccurrences is an unsupported type, check if it needs cleaning or further analysis Unsupported
associatedReferences is an unsupported type, check if it needs cleaning or further analysis Unsupported
associatedTaxa is an unsupported type, check if it needs cleaning or further analysis Unsupported
otherCatalogNumbers is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismID is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismName is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismScope is an unsupported type, check if it needs cleaning or further analysis Unsupported
associatedOrganisms is an unsupported type, check if it needs cleaning or further analysis Unsupported
previousIdentifications is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismRemarks is an unsupported type, check if it needs cleaning or further analysis Unsupported
materialEntityID is an unsupported type, check if it needs cleaning or further analysis Unsupported
materialEntityRemarks is an unsupported type, check if it needs cleaning or further analysis Unsupported
parentEventID is an unsupported type, check if it needs cleaning or further analysis Unsupported
eventType is an unsupported type, check if it needs cleaning or further analysis Unsupported
eventTime is an unsupported type, check if it needs cleaning or further analysis Unsupported
day is an unsupported type, check if it needs cleaning or further analysis Unsupported
habitat is an unsupported type, check if it needs cleaning or further analysis Unsupported
samplingProtocol is an unsupported type, check if it needs cleaning or further analysis Unsupported
sampleSizeValue is an unsupported type, check if it needs cleaning or further analysis Unsupported
sampleSizeUnit is an unsupported type, check if it needs cleaning or further analysis Unsupported
samplingEffort is an unsupported type, check if it needs cleaning or further analysis Unsupported
fieldNotes is an unsupported type, check if it needs cleaning or further analysis Unsupported
eventRemarks is an unsupported type, check if it needs cleaning or further analysis Unsupported
higherGeographyID is an unsupported type, check if it needs cleaning or further analysis Unsupported
municipality is an unsupported type, check if it needs cleaning or further analysis Unsupported
verbatimLocality is an unsupported type, check if it needs cleaning or further analysis Unsupported
verticalDatum is an unsupported type, check if it needs cleaning or further analysis Unsupported
maximumDistanceAboveSurfaceInMeters is an unsupported type, check if it needs cleaning or further analysis Unsupported
locationAccordingTo is an unsupported type, check if it needs cleaning or further analysis Unsupported
locationRemarks is an unsupported type, check if it needs cleaning or further analysis Unsupported
coordinatePrecision is an unsupported type, check if it needs cleaning or further analysis Unsupported
verbatimSRS is an unsupported type, check if it needs cleaning or further analysis Unsupported
footprintWKT is an unsupported type, check if it needs cleaning or further analysis Unsupported
footprintSRS is an unsupported type, check if it needs cleaning or further analysis Unsupported
footprintSpatialFit is an unsupported type, check if it needs cleaning or further analysis Unsupported
georeferencedDate is an unsupported type, check if it needs cleaning or further analysis Unsupported
georeferenceSources is an unsupported type, check if it needs cleaning or further analysis Unsupported
geologicalContextID is an unsupported type, check if it needs cleaning or further analysis Unsupported
earliestEonOrLowestEonothem is an unsupported type, check if it needs cleaning or further analysis Unsupported
earliestPeriodOrLowestSystem is an unsupported type, check if it needs cleaning or further analysis Unsupported
earliestEpochOrLowestSeries is an unsupported type, check if it needs cleaning or further analysis Unsupported
earliestAgeOrLowestStage is an unsupported type, check if it needs cleaning or further analysis Unsupported
latestAgeOrHighestStage is an unsupported type, check if it needs cleaning or further analysis Unsupported
lowestBiostratigraphicZone is an unsupported type, check if it needs cleaning or further analysis Unsupported
group is an unsupported type, check if it needs cleaning or further analysis Unsupported
formation is an unsupported type, check if it needs cleaning or further analysis Unsupported
bed is an unsupported type, check if it needs cleaning or further analysis Unsupported
identificationID is an unsupported type, check if it needs cleaning or further analysis Unsupported
dateIdentified is an unsupported type, check if it needs cleaning or further analysis Unsupported
identificationReferences is an unsupported type, check if it needs cleaning or further analysis Unsupported
scientificNameID is an unsupported type, check if it needs cleaning or further analysis Unsupported
nameAccordingToID is an unsupported type, check if it needs cleaning or further analysis Unsupported
namePublishedInYear is an unsupported type, check if it needs cleaning or further analysis Unsupported
tribe is an unsupported type, check if it needs cleaning or further analysis Unsupported
infragenericEpithet is an unsupported type, check if it needs cleaning or further analysis Unsupported
elevation is an unsupported type, check if it needs cleaning or further analysis Unsupported
elevationAccuracy is an unsupported type, check if it needs cleaning or further analysis Unsupported
subgenusKey is an unsupported type, check if it needs cleaning or further analysis Unsupported
typifiedName is an unsupported type, check if it needs cleaning or further analysis Unsupported
relativeOrganismQuantity is an unsupported type, check if it needs cleaning or further analysis Unsupported
projectId is an unsupported type, check if it needs cleaning or further analysis Unsupported
depthAccuracy has 39321 (8.6%) zeros Zeros

Reproduction

Analysis started2025-01-02 22:08:44.599184
Analysis finished2025-01-02 22:09:03.341483
Duration18.74 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

gbifID
Real number (ℝ)

Unique 

Distinct455212
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1508413676
Minimum1317202452
Maximum4987328293
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:03.432540image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1317202452
5-th percentile1317532562
Q11318851304
median1320500903
Q31322145331
95-th percentile3467268365
Maximum4987328293
Range3670125841
Interquartile range (IQR)3294027.25

Descriptive statistics

Standard deviation649700470.2
Coefficient of variation (CV)0.4307177008
Kurtosis13.90951695
Mean1508413676
Median Absolute Deviation (MAD)1647085
Skewness3.833544488
Sum6.866480061 × 1014
Variance4.22110701 × 1017
MonotonicityNot monotonic
2025-01-02T17:09:03.500257image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1321406230 1
 
< 0.1%
1317202656 1
 
< 0.1%
1317202715 1
 
< 0.1%
1322535976 1
 
< 0.1%
1317203467 1
 
< 0.1%
2235732924 1
 
< 0.1%
3758717622 1
 
< 0.1%
1317206835 1
 
< 0.1%
1322539466 1
 
< 0.1%
2235733055 1
 
< 0.1%
Other values (455202) 455202
> 99.9%
ValueCountFrequency (%)
1317202452 1
< 0.1%
1317202465 1
< 0.1%
1317202506 1
< 0.1%
1317202522 1
< 0.1%
1317202524 1
< 0.1%
ValueCountFrequency (%)
4987328293 1
< 0.1%
4987328290 1
< 0.1%
4987328243 1
< 0.1%
4987328221 1
< 0.1%
4987328186 1
< 0.1%

accessRights
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

bibliographicCitation
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

language
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

license
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:03.573111image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3186484
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCC0_1_0
2nd rowCC0_1_0
3rd rowCC0_1_0
4th rowCC0_1_0
5th rowCC0_1_0
ValueCountFrequency (%)
cc0_1_0 455212
100.0%
2025-01-02T17:09:03.706545image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 910424
28.6%
0 910424
28.6%
_ 910424
28.6%
1 455212
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3186484
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 910424
28.6%
0 910424
28.6%
_ 910424
28.6%
1 455212
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3186484
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 910424
28.6%
0 910424
28.6%
_ 910424
28.6%
1 455212
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3186484
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 910424
28.6%
0 910424
28.6%
_ 910424
28.6%
1 455212
14.3%
Distinct55507
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:03.811424image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters9104240
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31272 ?
Unique (%)6.9%

Sample

1st row2023-06-02T12:34:00Z
2nd row2019-11-27T11:21:00Z
3rd row2018-02-21T11:18:00Z
4th row2020-03-23T11:52:00Z
5th row2019-07-18T12:15:00Z
ValueCountFrequency (%)
2022-09-13t10:13:00z 2762
 
0.6%
2015-04-16t13:10:00z 2261
 
0.5%
2018-07-27t10:48:00z 2063
 
0.5%
2017-12-01t13:03:00z 2039
 
0.4%
2017-08-29t08:37:00z 1935
 
0.4%
2018-07-27t10:44:00z 1898
 
0.4%
2019-07-18t12:17:00z 1876
 
0.4%
2017-12-18t13:20:00z 1847
 
0.4%
2017-12-04t11:22:00z 1814
 
0.4%
2019-07-18t12:15:00z 1731
 
0.4%
Other values (55497) 434986
95.6%
2025-01-02T17:09:03.975144image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2223228
24.4%
1 1214237
13.3%
2 1142482
12.5%
- 910424
10.0%
: 910424
10.0%
T 455212
 
5.0%
Z 455212
 
5.0%
4 380598
 
4.2%
8 314110
 
3.5%
3 305429
 
3.4%
Other values (4) 792884
 
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9104240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2223228
24.4%
1 1214237
13.3%
2 1142482
12.5%
- 910424
10.0%
: 910424
10.0%
T 455212
 
5.0%
Z 455212
 
5.0%
4 380598
 
4.2%
8 314110
 
3.5%
3 305429
 
3.4%
Other values (4) 792884
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9104240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2223228
24.4%
1 1214237
13.3%
2 1142482
12.5%
- 910424
10.0%
: 910424
10.0%
T 455212
 
5.0%
Z 455212
 
5.0%
4 380598
 
4.2%
8 314110
 
3.5%
3 305429
 
3.4%
Other values (4) 792884
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9104240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2223228
24.4%
1 1214237
13.3%
2 1142482
12.5%
- 910424
10.0%
: 910424
10.0%
T 455212
 
5.0%
Z 455212
 
5.0%
4 380598
 
4.2%
8 314110
 
3.5%
3 305429
 
3.4%
Other values (4) 792884
 
8.7%

publisher
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:04.048390image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length59
Median length59
Mean length59
Min length59

Characters and Unicode

Total characters26857508
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNational Museum of Natural History, Smithsonian Institution
2nd rowNational Museum of Natural History, Smithsonian Institution
3rd rowNational Museum of Natural History, Smithsonian Institution
4th rowNational Museum of Natural History, Smithsonian Institution
5th rowNational Museum of Natural History, Smithsonian Institution
ValueCountFrequency (%)
national 455212
14.3%
museum 455212
14.3%
of 455212
14.3%
natural 455212
14.3%
history 455212
14.3%
smithsonian 455212
14.3%
institution 455212
14.3%
2025-01-02T17:09:04.162168image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 3186484
11.9%
i 2731272
10.2%
2731272
10.2%
o 2276060
 
8.5%
a 2276060
 
8.5%
n 2276060
 
8.5%
s 1820848
 
6.8%
u 1820848
 
6.8%
N 910424
 
3.4%
m 910424
 
3.4%
Other values (11) 5917756
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26857508
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 3186484
11.9%
i 2731272
10.2%
2731272
10.2%
o 2276060
 
8.5%
a 2276060
 
8.5%
n 2276060
 
8.5%
s 1820848
 
6.8%
u 1820848
 
6.8%
N 910424
 
3.4%
m 910424
 
3.4%
Other values (11) 5917756
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26857508
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 3186484
11.9%
i 2731272
10.2%
2731272
10.2%
o 2276060
 
8.5%
a 2276060
 
8.5%
n 2276060
 
8.5%
s 1820848
 
6.8%
u 1820848
 
6.8%
N 910424
 
3.4%
m 910424
 
3.4%
Other values (11) 5917756
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26857508
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 3186484
11.9%
i 2731272
10.2%
2731272
10.2%
o 2276060
 
8.5%
a 2276060
 
8.5%
n 2276060
 
8.5%
s 1820848
 
6.8%
u 1820848
 
6.8%
N 910424
 
3.4%
m 910424
 
3.4%
Other values (11) 5917756
22.0%

references
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

rightsHolder
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

type
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

institutionID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:04.229368image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters13201148
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowurn:lsid:biocol.org:col:34871
2nd rowurn:lsid:biocol.org:col:34871
3rd rowurn:lsid:biocol.org:col:34871
4th rowurn:lsid:biocol.org:col:34871
5th rowurn:lsid:biocol.org:col:34871
ValueCountFrequency (%)
urn:lsid:biocol.org:col:34871 455212
100.0%
2025-01-02T17:09:04.344241image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1820848
13.8%
: 1820848
13.8%
l 1365636
 
10.3%
r 910424
 
6.9%
c 910424
 
6.9%
i 910424
 
6.9%
u 455212
 
3.4%
s 455212
 
3.4%
d 455212
 
3.4%
n 455212
 
3.4%
Other values (8) 3641696
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13201148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1820848
13.8%
: 1820848
13.8%
l 1365636
 
10.3%
r 910424
 
6.9%
c 910424
 
6.9%
i 910424
 
6.9%
u 455212
 
3.4%
s 455212
 
3.4%
d 455212
 
3.4%
n 455212
 
3.4%
Other values (8) 3641696
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13201148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1820848
13.8%
: 1820848
13.8%
l 1365636
 
10.3%
r 910424
 
6.9%
c 910424
 
6.9%
i 910424
 
6.9%
u 455212
 
3.4%
s 455212
 
3.4%
d 455212
 
3.4%
n 455212
 
3.4%
Other values (8) 3641696
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13201148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1820848
13.8%
: 1820848
13.8%
l 1365636
 
10.3%
r 910424
 
6.9%
c 910424
 
6.9%
i 910424
 
6.9%
u 455212
 
3.4%
s 455212
 
3.4%
d 455212
 
3.4%
n 455212
 
3.4%
Other values (8) 3641696
27.6%

collectionID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:04.417503image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length45
Median length45
Mean length45
Min length45

Characters and Unicode

Total characters20484540
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowurn:uuid:09c9cf5f-f5d3-48cc-b5c8-cd9b9fbd631f
2nd rowurn:uuid:09c9cf5f-f5d3-48cc-b5c8-cd9b9fbd631f
3rd rowurn:uuid:09c9cf5f-f5d3-48cc-b5c8-cd9b9fbd631f
4th rowurn:uuid:09c9cf5f-f5d3-48cc-b5c8-cd9b9fbd631f
5th rowurn:uuid:09c9cf5f-f5d3-48cc-b5c8-cd9b9fbd631f
ValueCountFrequency (%)
urn:uuid:09c9cf5f-f5d3-48cc-b5c8-cd9b9fbd631f 455212
100.0%
2025-01-02T17:09:04.549100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 2731272
13.3%
f 2276060
11.1%
- 1820848
8.9%
9 1820848
8.9%
d 1820848
8.9%
u 1365636
 
6.7%
b 1365636
 
6.7%
5 1365636
 
6.7%
8 910424
 
4.4%
: 910424
 
4.4%
Other values (8) 4096908
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20484540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 2731272
13.3%
f 2276060
11.1%
- 1820848
8.9%
9 1820848
8.9%
d 1820848
8.9%
u 1365636
 
6.7%
b 1365636
 
6.7%
5 1365636
 
6.7%
8 910424
 
4.4%
: 910424
 
4.4%
Other values (8) 4096908
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20484540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 2731272
13.3%
f 2276060
11.1%
- 1820848
8.9%
9 1820848
8.9%
d 1820848
8.9%
u 1365636
 
6.7%
b 1365636
 
6.7%
5 1365636
 
6.7%
8 910424
 
4.4%
: 910424
 
4.4%
Other values (8) 4096908
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20484540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 2731272
13.3%
f 2276060
11.1%
- 1820848
8.9%
9 1820848
8.9%
d 1820848
8.9%
u 1365636
 
6.7%
b 1365636
 
6.7%
5 1365636
 
6.7%
8 910424
 
4.4%
: 910424
 
4.4%
Other values (8) 4096908
20.0%

datasetID
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

institutionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:04.591710image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1820848
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSNM
2nd rowUSNM
3rd rowUSNM
4th rowUSNM
5th rowUSNM
ValueCountFrequency (%)
usnm 455212
100.0%
2025-01-02T17:09:04.683555image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 455212
25.0%
S 455212
25.0%
N 455212
25.0%
M 455212
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1820848
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 455212
25.0%
S 455212
25.0%
N 455212
25.0%
M 455212
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1820848
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 455212
25.0%
S 455212
25.0%
N 455212
25.0%
M 455212
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1820848
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 455212
25.0%
S 455212
25.0%
N 455212
25.0%
M 455212
25.0%

collectionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:04.726130image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1820848
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFISH
2nd rowFISH
3rd rowFISH
4th rowFISH
5th rowFISH
ValueCountFrequency (%)
fish 455212
100.0%
2025-01-02T17:09:04.814348image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F 455212
25.0%
I 455212
25.0%
S 455212
25.0%
H 455212
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1820848
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 455212
25.0%
I 455212
25.0%
S 455212
25.0%
H 455212
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1820848
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 455212
25.0%
I 455212
25.0%
S 455212
25.0%
H 455212
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1820848
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 455212
25.0%
I 455212
25.0%
S 455212
25.0%
H 455212
25.0%

datasetName
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:04.874023image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters8649028
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNMNH Extant Biology
2nd rowNMNH Extant Biology
3rd rowNMNH Extant Biology
4th rowNMNH Extant Biology
5th rowNMNH Extant Biology
ValueCountFrequency (%)
nmnh 455212
33.3%
extant 455212
33.3%
biology 455212
33.3%
2025-01-02T17:09:04.982587image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 910424
 
10.5%
t 910424
 
10.5%
910424
 
10.5%
o 910424
 
10.5%
H 455212
 
5.3%
E 455212
 
5.3%
M 455212
 
5.3%
x 455212
 
5.3%
a 455212
 
5.3%
B 455212
 
5.3%
Other values (5) 2276060
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8649028
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 910424
 
10.5%
t 910424
 
10.5%
910424
 
10.5%
o 910424
 
10.5%
H 455212
 
5.3%
E 455212
 
5.3%
M 455212
 
5.3%
x 455212
 
5.3%
a 455212
 
5.3%
B 455212
 
5.3%
Other values (5) 2276060
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8649028
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 910424
 
10.5%
t 910424
 
10.5%
910424
 
10.5%
o 910424
 
10.5%
H 455212
 
5.3%
E 455212
 
5.3%
M 455212
 
5.3%
x 455212
 
5.3%
a 455212
 
5.3%
B 455212
 
5.3%
Other values (5) 2276060
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8649028
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 910424
 
10.5%
t 910424
 
10.5%
910424
 
10.5%
o 910424
 
10.5%
H 455212
 
5.3%
E 455212
 
5.3%
M 455212
 
5.3%
x 455212
 
5.3%
a 455212
 
5.3%
B 455212
 
5.3%
Other values (5) 2276060
26.3%

ownerInstitutionCode
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:05.041534image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length18
Mean length18.08130717
Min length18

Characters and Unicode

Total characters8230828
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRESERVED_SPECIMEN
2nd rowPRESERVED_SPECIMEN
3rd rowPRESERVED_SPECIMEN
4th rowPRESERVED_SPECIMEN
5th rowMACHINE_OBSERVATION
ValueCountFrequency (%)
preserved_specimen 418200
91.9%
machine_observation 37012
 
8.1%
2025-01-02T17:09:05.154335image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 2165024
26.3%
R 873412
10.6%
S 873412
10.6%
P 836400
 
10.2%
N 492224
 
6.0%
I 492224
 
6.0%
V 455212
 
5.5%
_ 455212
 
5.5%
M 455212
 
5.5%
C 455212
 
5.5%
Other values (6) 677284
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8230828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 2165024
26.3%
R 873412
10.6%
S 873412
10.6%
P 836400
 
10.2%
N 492224
 
6.0%
I 492224
 
6.0%
V 455212
 
5.5%
_ 455212
 
5.5%
M 455212
 
5.5%
C 455212
 
5.5%
Other values (6) 677284
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8230828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 2165024
26.3%
R 873412
10.6%
S 873412
10.6%
P 836400
 
10.2%
N 492224
 
6.0%
I 492224
 
6.0%
V 455212
 
5.5%
_ 455212
 
5.5%
M 455212
 
5.5%
C 455212
 
5.5%
Other values (6) 677284
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8230828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 2165024
26.3%
R 873412
10.6%
S 873412
10.6%
P 836400
 
10.2%
N 492224
 
6.0%
I 492224
 
6.0%
V 455212
 
5.5%
_ 455212
 
5.5%
M 455212
 
5.5%
C 455212
 
5.5%
Other values (6) 677284
 
8.2%

informationWithheld
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

dataGeneralizations
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

dynamicProperties
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

occurrenceID
Text

Unique 

Distinct455212
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:05.396815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length63
Median length63
Mean length63
Min length63

Characters and Unicode

Total characters28678356
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique455212 ?
Unique (%)100.0%

Sample

1st rowhttp://n2t.net/ark:/65665/30002bab5-5433-4b6c-8496-286a4a697fd7
2nd rowhttp://n2t.net/ark:/65665/3000315ff-b613-4f47-813c-5c48d8e0a883
3rd rowhttp://n2t.net/ark:/65665/3ebef4ab3-d946-4961-9221-c7c9692640f8
4th rowhttp://n2t.net/ark:/65665/3000bbb81-e139-47f8-b2bc-db762804769d
5th rowhttp://n2t.net/ark:/65665/3002333ca-4702-4d0d-93cd-265885eff56a
ValueCountFrequency (%)
http://n2t.net/ark:/65665/3ec57b74b-7fc8-4006-ad93-945fb0784573 1
 
< 0.1%
http://n2t.net/ark:/65665/3b9e9dddb-ff7a-43f7-ac4c-9619ba4ad5e5 1
 
< 0.1%
http://n2t.net/ark:/65665/30002bab5-5433-4b6c-8496-286a4a697fd7 1
 
< 0.1%
http://n2t.net/ark:/65665/3000315ff-b613-4f47-813c-5c48d8e0a883 1
 
< 0.1%
http://n2t.net/ark:/65665/3ebef4ab3-d946-4961-9221-c7c9692640f8 1
 
< 0.1%
http://n2t.net/ark:/65665/3000bbb81-e139-47f8-b2bc-db762804769d 1
 
< 0.1%
http://n2t.net/ark:/65665/3002333ca-4702-4d0d-93cd-265885eff56a 1
 
< 0.1%
http://n2t.net/ark:/65665/3ec0c9d7d-35a5-4841-8a5f-f74ef9113d06 1
 
< 0.1%
http://n2t.net/ark:/65665/300319b93-d6b8-4b79-b23d-d3825483b706 1
 
< 0.1%
http://n2t.net/ark:/65665/3ec168a54-17b9-4a71-9ecc-92d446311c64 1
 
< 0.1%
Other values (455202) 455202
> 99.9%
2025-01-02T17:09:05.781071image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 2276060
 
7.9%
6 2219127
 
7.7%
- 1820848
 
6.3%
t 1820848
 
6.3%
5 1762661
 
6.1%
a 1423503
 
5.0%
2 1308759
 
4.6%
4 1308747
 
4.6%
e 1308683
 
4.6%
3 1308534
 
4.6%
Other values (16) 12120586
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28678356
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 2276060
 
7.9%
6 2219127
 
7.7%
- 1820848
 
6.3%
t 1820848
 
6.3%
5 1762661
 
6.1%
a 1423503
 
5.0%
2 1308759
 
4.6%
4 1308747
 
4.6%
e 1308683
 
4.6%
3 1308534
 
4.6%
Other values (16) 12120586
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28678356
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 2276060
 
7.9%
6 2219127
 
7.7%
- 1820848
 
6.3%
t 1820848
 
6.3%
5 1762661
 
6.1%
a 1423503
 
5.0%
2 1308759
 
4.6%
4 1308747
 
4.6%
e 1308683
 
4.6%
3 1308534
 
4.6%
Other values (16) 12120586
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28678356
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 2276060
 
7.9%
6 2219127
 
7.7%
- 1820848
 
6.3%
t 1820848
 
6.3%
5 1762661
 
6.1%
a 1423503
 
5.0%
2 1308759
 
4.6%
4 1308747
 
4.6%
e 1308683
 
4.6%
3 1308534
 
4.6%
Other values (16) 12120586
42.3%
Distinct455204
Distinct (%)> 99.9%
Missing3
Missing (%)< 0.1%
Memory size3.5 MiB
2025-01-02T17:09:06.073049image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length14
Median length11
Mean length11.04621613
Min length6

Characters and Unicode

Total characters5028337
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique455199 ?
Unique (%)> 99.9%

Sample

1st rowUSNM 51082
2nd rowUSNM 110432
3rd rowUSNM 49860
4th rowUSNM 239751
5th rowUSNM RAD122557
ValueCountFrequency (%)
usnm 455209
50.0%
465983 2
 
< 0.1%
114351 2
 
< 0.1%
135878 2
 
< 0.1%
466814 2
 
< 0.1%
rad125895 2
 
< 0.1%
282636 1
 
< 0.1%
36169 1
 
< 0.1%
459059 1
 
< 0.1%
212744 1
 
< 0.1%
Other values (455195) 455195
50.0%
2025-01-02T17:09:06.414912image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 464924
 
9.2%
U 455209
 
9.1%
S 455209
 
9.1%
M 455209
 
9.1%
455209
 
9.1%
1 348722
 
6.9%
2 335206
 
6.7%
3 330422
 
6.6%
4 286516
 
5.7%
6 228754
 
4.5%
Other values (10) 1212957
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5028337
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 464924
 
9.2%
U 455209
 
9.1%
S 455209
 
9.1%
M 455209
 
9.1%
455209
 
9.1%
1 348722
 
6.9%
2 335206
 
6.7%
3 330422
 
6.6%
4 286516
 
5.7%
6 228754
 
4.5%
Other values (10) 1212957
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5028337
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 464924
 
9.2%
U 455209
 
9.1%
S 455209
 
9.1%
M 455209
 
9.1%
455209
 
9.1%
1 348722
 
6.9%
2 335206
 
6.7%
3 330422
 
6.6%
4 286516
 
5.7%
6 228754
 
4.5%
Other values (10) 1212957
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5028337
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 464924
 
9.2%
U 455209
 
9.1%
S 455209
 
9.1%
M 455209
 
9.1%
455209
 
9.1%
1 348722
 
6.9%
2 335206
 
6.7%
3 330422
 
6.6%
4 286516
 
5.7%
6 228754
 
4.5%
Other values (10) 1212957
24.1%

recordNumber
Text

Missing 

Distinct20814
Distinct (%)99.9%
Missing434386
Missing (%)95.4%
Memory size3.5 MiB
2025-01-02T17:09:06.594228image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length42
Median length8
Mean length8.388456737
Min length1

Characters and Unicode

Total characters174698
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20803 ?
Unique (%)99.9%

Sample

1st rowPHISH-032
2nd rowAUST-251
3rd rowMOC11646
4th rowRP-202
5th rowSCIL-052
ValueCountFrequency (%)
blz 1430
 
5.5%
bah 710
 
2.8%
tci 681
 
2.6%
sms 536
 
2.1%
cur 426
 
1.7%
tob 393
 
1.5%
twn 280
 
1.1%
hbb 157
 
0.6%
fcc 146
 
0.6%
keb&mgg 111
 
0.4%
Other values (18988) 20921
81.1%
2025-01-02T17:09:06.828642image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 16672
 
9.5%
0 13195
 
7.6%
- 11059
 
6.3%
2 9655
 
5.5%
3 7495
 
4.3%
7 6732
 
3.9%
4 6594
 
3.8%
9 6304
 
3.6%
S 6075
 
3.5%
I 5691
 
3.3%
Other values (54) 85226
48.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 174698
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 16672
 
9.5%
0 13195
 
7.6%
- 11059
 
6.3%
2 9655
 
5.5%
3 7495
 
4.3%
7 6732
 
3.9%
4 6594
 
3.8%
9 6304
 
3.6%
S 6075
 
3.5%
I 5691
 
3.3%
Other values (54) 85226
48.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 174698
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 16672
 
9.5%
0 13195
 
7.6%
- 11059
 
6.3%
2 9655
 
5.5%
3 7495
 
4.3%
7 6732
 
3.9%
4 6594
 
3.8%
9 6304
 
3.6%
S 6075
 
3.5%
I 5691
 
3.3%
Other values (54) 85226
48.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 174698
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 16672
 
9.5%
0 13195
 
7.6%
- 11059
 
6.3%
2 9655
 
5.5%
3 7495
 
4.3%
7 6732
 
3.9%
4 6594
 
3.8%
9 6304
 
3.6%
S 6075
 
3.5%
I 5691
 
3.3%
Other values (54) 85226
48.8%

recordedBy
Text

Missing 

Distinct7883
Distinct (%)4.7%
Missing287312
Missing (%)63.1%
Memory size3.5 MiB
2025-01-02T17:09:06.976354image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length240
Median length115
Mean length26.11823109
Min length1

Characters and Unicode

Total characters4385251
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3022 ?
Unique (%)1.8%

Sample

1st rowJ. Snyder
2nd rowD. Richardson
3rd rowSmithsonian Team, A. Alcala & Silliman University Group
4th rowBronson
5th rowG. Hendler
ValueCountFrequency (%)
77670
 
9.1%
j 42738
 
5.0%
m 36874
 
4.3%
d 28293
 
3.3%
r 27606
 
3.2%
c 22145
 
2.6%
l 20146
 
2.3%
h 19636
 
2.3%
s 18374
 
2.1%
a 17770
 
2.1%
Other values (4981) 546427
63.7%
2025-01-02T17:09:07.209603image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
689779
15.7%
. 354996
 
8.1%
e 287100
 
6.5%
a 267898
 
6.1%
r 202145
 
4.6%
n 201834
 
4.6%
i 198853
 
4.5%
o 170908
 
3.9%
l 161879
 
3.7%
t 156528
 
3.6%
Other values (66) 1693331
38.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4385251
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
689779
15.7%
. 354996
 
8.1%
e 287100
 
6.5%
a 267898
 
6.1%
r 202145
 
4.6%
n 201834
 
4.6%
i 198853
 
4.5%
o 170908
 
3.9%
l 161879
 
3.7%
t 156528
 
3.6%
Other values (66) 1693331
38.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4385251
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
689779
15.7%
. 354996
 
8.1%
e 287100
 
6.5%
a 267898
 
6.1%
r 202145
 
4.6%
n 201834
 
4.6%
i 198853
 
4.5%
o 170908
 
3.9%
l 161879
 
3.7%
t 156528
 
3.6%
Other values (66) 1693331
38.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4385251
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
689779
15.7%
. 354996
 
8.1%
e 287100
 
6.5%
a 267898
 
6.1%
r 202145
 
4.6%
n 201834
 
4.6%
i 198853
 
4.5%
o 170908
 
3.9%
l 161879
 
3.7%
t 156528
 
3.6%
Other values (66) 1693331
38.6%

recordedByID
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

individualCount
Real number (ℝ)

Skewed 

Distinct619
Distinct (%)0.1%
Missing15
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean6.127355848
Minimum0
Maximum5911
Zeros3187
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:07.276746image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q34
95-th percentile21
Maximum5911
Range5911
Interquartile range (IQR)3

Descriptive statistics

Standard deviation33.09642674
Coefficient of variation (CV)5.401420705
Kurtosis5798.372688
Mean6.127355848
Median Absolute Deviation (MAD)0
Skewness51.8804012
Sum2789154
Variance1095.373463
MonotonicityNot monotonic
2025-01-02T17:09:07.340539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 245716
54.0%
2 61709
 
13.6%
3 30726
 
6.7%
4 19436
 
4.3%
5 14092
 
3.1%
6 10260
 
2.3%
7 7454
 
1.6%
10 6775
 
1.5%
8 6055
 
1.3%
9 4855
 
1.1%
Other values (609) 48119
 
10.6%
ValueCountFrequency (%)
0 3187
 
0.7%
1 245716
54.0%
2 61709
 
13.6%
3 30726
 
6.7%
4 19436
 
4.3%
ValueCountFrequency (%)
5911 1
< 0.1%
5453 1
< 0.1%
4444 1
< 0.1%
3384 1
< 0.1%
3082 1
< 0.1%

organismQuantity
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

organismQuantityType
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

sex
Text

Constant  Missing 

Distinct1
Distinct (%)33.3%
Missing455209
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:07.389748image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters12
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMALE
2nd rowMALE
3rd rowMALE
ValueCountFrequency (%)
male 3
100.0%
2025-01-02T17:09:07.484143image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 3
25.0%
A 3
25.0%
L 3
25.0%
E 3
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 3
25.0%
A 3
25.0%
L 3
25.0%
E 3
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 3
25.0%
A 3
25.0%
L 3
25.0%
E 3
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 3
25.0%
A 3
25.0%
L 3
25.0%
E 3
25.0%

lifeStage
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

reproductiveCondition
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

caste
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

behavior
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

vitality
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

establishmentMeans
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

degreeOfEstablishment
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

pathway
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

georeferenceVerificationStatus
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:07.534714image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.999995606
Min length6

Characters and Unicode

Total characters3186482
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRESENT
2nd rowPRESENT
3rd rowPRESENT
4th rowPRESENT
5th rowPRESENT
ValueCountFrequency (%)
present 455210
> 99.9%
absent 2
 
< 0.1%
2025-01-02T17:09:07.644053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 910422
28.6%
S 455212
14.3%
T 455212
14.3%
N 455212
14.3%
P 455210
14.3%
R 455210
14.3%
A 2
 
< 0.1%
B 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3186482
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 910422
28.6%
S 455212
14.3%
T 455212
14.3%
N 455212
14.3%
P 455210
14.3%
R 455210
14.3%
A 2
 
< 0.1%
B 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3186482
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 910422
28.6%
S 455212
14.3%
T 455212
14.3%
N 455212
14.3%
P 455210
14.3%
R 455210
14.3%
A 2
 
< 0.1%
B 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3186482
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 910422
28.6%
S 455212
14.3%
T 455212
14.3%
N 455212
14.3%
P 455210
14.3%
R 455210
14.3%
A 2
 
< 0.1%
B 2
 
< 0.1%

preparations
Text

Missing 

Distinct325
Distinct (%)0.3%
Missing346184
Missing (%)76.0%
Memory size3.5 MiB
2025-01-02T17:09:07.719525image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length255
Median length192
Mean length11.80351836
Min length4

Characters and Unicode

Total characters1286914
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique141 ?
Unique (%)0.1%

Sample

1st rowDry Osteological Specimen
2nd rowGlycerin with Bone Stain
3rd rowPolyester
4th rowLarvae [ETOH Fixed]
5th rowUnknown
ValueCountFrequency (%)
larvae 25640
14.6%
polyester 20066
 
11.4%
photograph 14070
 
8.0%
unknown 11506
 
6.6%
film 9617
 
5.5%
specimen 8056
 
4.6%
osteological 7025
 
4.0%
glycerin 7019
 
4.0%
with 7017
 
4.0%
stain 7012
 
4.0%
Other values (60) 58274
33.2%
2025-01-02T17:09:07.876495image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 130540
 
10.1%
a 117241
 
9.1%
o 94955
 
7.4%
r 91527
 
7.1%
t 83181
 
6.5%
n 69625
 
5.4%
l 66760
 
5.2%
i 66635
 
5.2%
66274
 
5.1%
h 41878
 
3.3%
Other values (46) 458298
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1286914
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 130540
 
10.1%
a 117241
 
9.1%
o 94955
 
7.4%
r 91527
 
7.1%
t 83181
 
6.5%
n 69625
 
5.4%
l 66760
 
5.2%
i 66635
 
5.2%
66274
 
5.1%
h 41878
 
3.3%
Other values (46) 458298
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1286914
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 130540
 
10.1%
a 117241
 
9.1%
o 94955
 
7.4%
r 91527
 
7.1%
t 83181
 
6.5%
n 69625
 
5.4%
l 66760
 
5.2%
i 66635
 
5.2%
66274
 
5.1%
h 41878
 
3.3%
Other values (46) 458298
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1286914
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 130540
 
10.1%
a 117241
 
9.1%
o 94955
 
7.4%
r 91527
 
7.1%
t 83181
 
6.5%
n 69625
 
5.4%
l 66760
 
5.2%
i 66635
 
5.2%
66274
 
5.1%
h 41878
 
3.3%
Other values (46) 458298
35.6%

disposition
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

associatedOccurrences
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

associatedReferences
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

associatedSequences
Text

Missing 

Distinct447
Distinct (%)99.3%
Missing454762
Missing (%)99.9%
Memory size3.5 MiB
2025-01-02T17:09:07.973432image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length249
Median length49
Mean length59.88888889
Min length49

Characters and Unicode

Total characters26950
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique444 ?
Unique (%)98.7%

Sample

1st rowhttps://www.ncbi.nlm.nih.gov/gquery?term=FJ609901
2nd rowhttps://www.ncbi.nlm.nih.gov/gquery?term=HQ600890
3rd rowhttps://www.ncbi.nlm.nih.gov/gquery?term=HM748411
4th rowhttps://www.ncbi.nlm.nih.gov/gquery?term=HQ600884
5th rowhttps://www.ncbi.nlm.nih.gov/gquery?term=MN621852
ValueCountFrequency (%)
https://www.ncbi.nlm.nih.gov/gquery?term=hq543049 2
 
0.4%
https://www.ncbi.nlm.nih.gov/gquery?term=hq543050 2
 
0.4%
https://www.ncbi.nlm.nih.gov/gquery?term=mn549761 2
 
0.4%
https://www.ncbi.nlm.nih.gov/gquery?term=mn549745 1
 
0.2%
https://www.ncbi.nlm.nih.gov/gquery?term=fj609874 1
 
0.2%
https://www.ncbi.nlm.nih.gov/gquery?term=mn549729 1
 
0.2%
https://www.ncbi.nlm.nih.gov/gquery?term=gq367328 1
 
0.2%
https://www.ncbi.nlm.nih.gov/gquery?term=mn549747 1
 
0.2%
https://www.ncbi.nlm.nih.gov/gquery?term=af243926;https://www.ncbi.nlm.nih.gov/gquery?term=af244077;https://www.ncbi.nlm.nih.gov/gquery?term=af244002 1
 
0.2%
https://www.ncbi.nlm.nih.gov/gquery?term=mn549734 1
 
0.2%
Other values (437) 437
97.1%
2025-01-02T17:09:08.126047image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2192
 
8.1%
w 1644
 
6.1%
t 1644
 
6.1%
n 1644
 
6.1%
/ 1644
 
6.1%
m 1096
 
4.1%
h 1096
 
4.1%
i 1096
 
4.1%
g 1096
 
4.1%
r 1096
 
4.1%
Other values (41) 12702
47.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26950
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 2192
 
8.1%
w 1644
 
6.1%
t 1644
 
6.1%
n 1644
 
6.1%
/ 1644
 
6.1%
m 1096
 
4.1%
h 1096
 
4.1%
i 1096
 
4.1%
g 1096
 
4.1%
r 1096
 
4.1%
Other values (41) 12702
47.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26950
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 2192
 
8.1%
w 1644
 
6.1%
t 1644
 
6.1%
n 1644
 
6.1%
/ 1644
 
6.1%
m 1096
 
4.1%
h 1096
 
4.1%
i 1096
 
4.1%
g 1096
 
4.1%
r 1096
 
4.1%
Other values (41) 12702
47.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26950
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 2192
 
8.1%
w 1644
 
6.1%
t 1644
 
6.1%
n 1644
 
6.1%
/ 1644
 
6.1%
m 1096
 
4.1%
h 1096
 
4.1%
i 1096
 
4.1%
g 1096
 
4.1%
r 1096
 
4.1%
Other values (41) 12702
47.1%

associatedTaxa
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

otherCatalogNumbers
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

occurrenceRemarks
Text

Missing 

Distinct80317
Distinct (%)48.8%
Missing290485
Missing (%)63.8%
Memory size3.5 MiB
2025-01-02T17:09:08.295762image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length220698
Median length28059
Mean length75.76284398
Min length1

Characters and Unicode

Total characters12480186
Distinct characters122
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70648 ?
Unique (%)42.9%

Sample

1st rowNote in ledger: " pair of otoliths"; Otoliths are stored in the Osteo Collection.; Stored in Osteo Collection.; The ototliths are stored in Mugil Box 1 of 1, which contains catalog numbers: 110428, 110429, 110430, 110431, 110432, 110433, 110434, 110435, 110436, 110438, 110439, 110440, and 110441.
2nd rowCat. no. 105
3rd rowHost-bohadschia argus. rec from: truett, d. f.
4th rowSpecimen measurements as written on the slide mount: SL (mm)= 205; TL (mm)= 10" (254); This material is part of the John and Helen Randall Slide Collection. The slides were digitized October 2017. The Randall donation includes all intellectual property rights.; Black paint/goop on the film. Not obscuring specimen.
5th rowSpecimen measurements as written on the slide mount: SL (mm)= 57; TL (mm)= 2.8" (71); This material is part of the John and Helen Randall Slide Collection. The slides were digitized October 2017. The Randall donation includes all intellectual property rights.
ValueCountFrequency (%)
the 73553
 
3.9%
of 50804
 
2.7%
in 34960
 
1.8%
and 29482
 
1.6%
mm 26673
 
1.4%
collection 24234
 
1.3%
specimen 23152
 
1.2%
as 22917
 
1.2%
is 22746
 
1.2%
1 22640
 
1.2%
Other values (81634) 1569987
82.6%
2025-01-02T17:09:08.543208image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1609957
 
12.9%
e 933339
 
7.5%
a 635649
 
5.1%
i 628891
 
5.0%
t 615692
 
4.9%
n 591012
 
4.7%
o 588776
 
4.7%
s 465772
 
3.7%
l 461424
 
3.7%
r 452874
 
3.6%
Other values (112) 5496800
44.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12480186
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1609957
 
12.9%
e 933339
 
7.5%
a 635649
 
5.1%
i 628891
 
5.0%
t 615692
 
4.9%
n 591012
 
4.7%
o 588776
 
4.7%
s 465772
 
3.7%
l 461424
 
3.7%
r 452874
 
3.6%
Other values (112) 5496800
44.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12480186
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1609957
 
12.9%
e 933339
 
7.5%
a 635649
 
5.1%
i 628891
 
5.0%
t 615692
 
4.9%
n 591012
 
4.7%
o 588776
 
4.7%
s 465772
 
3.7%
l 461424
 
3.7%
r 452874
 
3.6%
Other values (112) 5496800
44.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12480186
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1609957
 
12.9%
e 933339
 
7.5%
a 635649
 
5.1%
i 628891
 
5.0%
t 615692
 
4.9%
n 591012
 
4.7%
o 588776
 
4.7%
s 465772
 
3.7%
l 461424
 
3.7%
r 452874
 
3.6%
Other values (112) 5496800
44.0%

organismID
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

organismName
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

organismScope
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

associatedOrganisms
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

previousIdentifications
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

organismRemarks
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

materialEntityID
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

materialEntityRemarks
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

verbatimLabel
Real number (ℝ)

Missing 

Distinct3
Distinct (%)100.0%
Missing455209
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean2.770833333
Minimum-9.3883
Maximum10.6925
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size3.5 MiB
2025-01-02T17:09:08.599787image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-9.3883
5-th percentile-7.74864
Q1-1.19
median7.0083
Q38.8504
95-th percentile10.32408
Maximum10.6925
Range20.0808
Interquartile range (IQR)10.0404

Descriptive statistics

Standard deviation10.69002923
Coefficient of variation (CV)3.858055663
Kurtosisnan
Mean2.770833333
Median Absolute Deviation (MAD)3.6842
Skewness-1.50349267
Sum8.3125
Variance114.276725
MonotonicityNot monotonic
2025-01-02T17:09:08.651089image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
-9.3883 1
 
< 0.1%
10.6925 1
 
< 0.1%
7.0083 1
 
< 0.1%
(Missing) 455209
> 99.9%
ValueCountFrequency (%)
-9.3883 1
< 0.1%
7.0083 1
< 0.1%
10.6925 1
< 0.1%
ValueCountFrequency (%)
10.6925 1
< 0.1%
7.0083 1
< 0.1%
-9.3883 1
< 0.1%

materialSampleID
Real number (ℝ)

Missing 

Distinct3
Distinct (%)100.0%
Missing455209
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean108.9917667
Minimum46.2133
Maximum158.199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:08.701030image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum46.2133
5-th percentile53.84827
Q184.38815
median122.563
Q3140.381
95-th percentile154.6354
Maximum158.199
Range111.9857
Interquartile range (IQR)55.99285

Descriptive statistics

Standard deviation57.21304949
Coefficient of variation (CV)0.524930013
Kurtosisnan
Mean108.9917667
Median Absolute Deviation (MAD)35.636
Skewness-1.007363459
Sum326.9753
Variance3273.333032
MonotonicityStrictly increasing
2025-01-02T17:09:08.746574image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
46.2133 1
 
< 0.1%
122.563 1
 
< 0.1%
158.199 1
 
< 0.1%
(Missing) 455209
> 99.9%
ValueCountFrequency (%)
46.2133 1
< 0.1%
122.563 1
< 0.1%
158.199 1
< 0.1%
ValueCountFrequency (%)
158.199 1
< 0.1%
122.563 1
< 0.1%
46.2133 1
< 0.1%

eventID
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing455211
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean941
Minimum941
Maximum941
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:08.797054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum941
5-th percentile941
Q1941
median941
Q3941
95-th percentile941
Maximum941
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean941
Median Absolute Deviation (MAD)0
Skewnessnan
Sum941
Variancenan
MonotonicityStrictly increasing
2025-01-02T17:09:08.841105image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
941 1
 
< 0.1%
(Missing) 455211
> 99.9%
ValueCountFrequency (%)
941 1
< 0.1%
ValueCountFrequency (%)
941 1
< 0.1%

parentEventID
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

eventType
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

fieldNumber
Text

Missing 

Distinct25291
Distinct (%)14.0%
Missing274211
Missing (%)60.2%
Memory size3.5 MiB
2025-01-02T17:09:08.995483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length149
Median length70
Mean length10.07362943
Min length1

Characters and Unicode

Total characters1823337
Distinct characters82
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10523 ?
Unique (%)5.8%

Sample

1st rowFJS-455
2nd rowM10-97B4 (40-60m)
3rd rowSP 78-18
4th rowBBC 1734 A; M-84
5th rowPHISH-2016-05; SIA-06
ValueCountFrequency (%)
vgs 19290
 
5.7%
jtw 14298
 
4.2%
bbc 6125
 
1.8%
lwk 4274
 
1.3%
lk 4258
 
1.3%
sol 3414
 
1.0%
rpv 3291
 
1.0%
sp 3134
 
0.9%
bayley 2740
 
0.8%
lrp 2643
 
0.8%
Other values (22433) 275090
81.3%
2025-01-02T17:09:09.336099image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 190028
 
10.4%
157556
 
8.6%
1 125554
 
6.9%
0 109950
 
6.0%
2 103376
 
5.7%
9 89324
 
4.9%
6 82127
 
4.5%
7 76841
 
4.2%
3 73150
 
4.0%
8 68610
 
3.8%
Other values (72) 746821
41.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1823337
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 190028
 
10.4%
157556
 
8.6%
1 125554
 
6.9%
0 109950
 
6.0%
2 103376
 
5.7%
9 89324
 
4.9%
6 82127
 
4.5%
7 76841
 
4.2%
3 73150
 
4.0%
8 68610
 
3.8%
Other values (72) 746821
41.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1823337
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 190028
 
10.4%
157556
 
8.6%
1 125554
 
6.9%
0 109950
 
6.0%
2 103376
 
5.7%
9 89324
 
4.9%
6 82127
 
4.5%
7 76841
 
4.2%
3 73150
 
4.0%
8 68610
 
3.8%
Other values (72) 746821
41.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1823337
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 190028
 
10.4%
157556
 
8.6%
1 125554
 
6.9%
0 109950
 
6.0%
2 103376
 
5.7%
9 89324
 
4.9%
6 82127
 
4.5%
7 76841
 
4.2%
3 73150
 
4.0%
8 68610
 
3.8%
Other values (72) 746821
41.0%

eventDate
Text

Missing 

Distinct30514
Distinct (%)7.7%
Missing60241
Missing (%)13.2%
Memory size3.5 MiB
2025-01-02T17:09:09.488753image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length10
Mean length10.09004712
Min length4

Characters and Unicode

Total characters3985276
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8111 ?
Unique (%)2.1%

Sample

1st row1938-03-25
2nd row1956-05-30
3rd row1997-05-10
4th row1978-05-22
5th row1928-02-10
ValueCountFrequency (%)
1906 1477
 
0.4%
1902 1141
 
0.3%
1888 1112
 
0.3%
1889 938
 
0.2%
1994-05-06 927
 
0.2%
1994-04-30 702
 
0.2%
1901 595
 
0.2%
1880 568
 
0.1%
1970-09-11/1970-09-16 510
 
0.1%
1893 440
 
0.1%
Other values (30504) 386561
97.9%
2025-01-02T17:09:09.708144image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 773124
19.4%
1 729306
18.3%
0 645975
16.2%
9 528070
13.3%
2 287210
 
7.2%
8 212491
 
5.3%
6 183136
 
4.6%
7 181671
 
4.6%
5 151243
 
3.8%
3 141325
 
3.5%
Other values (2) 151725
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3985276
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 773124
19.4%
1 729306
18.3%
0 645975
16.2%
9 528070
13.3%
2 287210
 
7.2%
8 212491
 
5.3%
6 183136
 
4.6%
7 181671
 
4.6%
5 151243
 
3.8%
3 141325
 
3.5%
Other values (2) 151725
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3985276
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 773124
19.4%
1 729306
18.3%
0 645975
16.2%
9 528070
13.3%
2 287210
 
7.2%
8 212491
 
5.3%
6 183136
 
4.6%
7 181671
 
4.6%
5 151243
 
3.8%
3 141325
 
3.5%
Other values (2) 151725
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3985276
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 773124
19.4%
1 729306
18.3%
0 645975
16.2%
9 528070
13.3%
2 287210
 
7.2%
8 212491
 
5.3%
6 183136
 
4.6%
7 181671
 
4.6%
5 151243
 
3.8%
3 141325
 
3.5%
Other values (2) 151725
 
3.8%

eventTime
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

startDayOfYear
Real number (ℝ)

Missing 

Distinct366
Distinct (%)0.1%
Missing91500
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean181.4506835
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:09.779127image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile32
Q1110
median173
Q3256
95-th percentile333
Maximum366
Range365
Interquartile range (IQR)146

Descriptive statistics

Standard deviation93.45936124
Coefficient of variation (CV)0.5150675623
Kurtosis-1.011902062
Mean181.4506835
Median Absolute Deviation (MAD)75
Skewness0.05860365426
Sum65995791
Variance8734.652203
MonotonicityNot monotonic
2025-01-02T17:09:09.847130image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126 2409
 
0.5%
251 1989
 
0.4%
120 1928
 
0.4%
117 1868
 
0.4%
146 1854
 
0.4%
159 1853
 
0.4%
143 1786
 
0.4%
161 1783
 
0.4%
141 1777
 
0.4%
154 1775
 
0.4%
Other values (356) 344690
75.7%
(Missing) 91500
 
20.1%
ValueCountFrequency (%)
1 253
0.1%
2 302
0.1%
3 330
0.1%
4 557
0.1%
5 542
0.1%
ValueCountFrequency (%)
366 139
 
< 0.1%
365 226
< 0.1%
364 325
0.1%
363 492
0.1%
362 261
0.1%

endDayOfYear
Real number (ℝ)

Missing 

Distinct366
Distinct (%)0.1%
Missing91500
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean181.7330525
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:09.912638image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile33
Q1111
median173
Q3257
95-th percentile333
Maximum366
Range365
Interquartile range (IQR)146

Descriptive statistics

Standard deviation93.46622768
Coefficient of variation (CV)0.5143050556
Kurtosis-1.011361402
Mean181.7330525
Median Absolute Deviation (MAD)75
Skewness0.05759645239
Sum66098492
Variance8735.935717
MonotonicityNot monotonic
2025-01-02T17:09:09.981218image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126 2497
 
0.5%
251 1977
 
0.4%
120 1908
 
0.4%
117 1873
 
0.4%
161 1803
 
0.4%
116 1783
 
0.4%
141 1778
 
0.4%
159 1755
 
0.4%
146 1754
 
0.4%
145 1748
 
0.4%
Other values (356) 344836
75.8%
(Missing) 91500
 
20.1%
ValueCountFrequency (%)
1 213
 
< 0.1%
2 289
0.1%
3 342
0.1%
4 546
0.1%
5 541
0.1%
ValueCountFrequency (%)
366 147
 
< 0.1%
365 247
0.1%
364 352
0.1%
363 465
0.1%
362 267
0.1%

year
Real number (ℝ)

Missing 

Distinct191
Distinct (%)< 0.1%
Missing60500
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean1959.718286
Minimum1800
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:10.050092image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1800
5-th percentile1893
Q11937
median1968
Q31985
95-th percentile2009
Maximum2024
Range224
Interquartile range (IQR)48

Descriptive statistics

Standard deviation35.45981754
Coefficient of variation (CV)0.01809434438
Kurtosis-0.5153656346
Mean1959.718286
Median Absolute Deviation (MAD)22
Skewness-0.6047226487
Sum773524324
Variance1257.39866
MonotonicityNot monotonic
2025-01-02T17:09:10.113531image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1909 17685
 
3.9%
1908 13638
 
3.0%
1970 11215
 
2.5%
1969 10429
 
2.3%
1964 9054
 
2.0%
1978 8877
 
2.0%
1967 8372
 
1.8%
1979 7846
 
1.7%
1971 7828
 
1.7%
1968 7271
 
1.6%
Other values (181) 292497
64.3%
(Missing) 60500
 
13.3%
ValueCountFrequency (%)
1800 2
< 0.1%
1812 1
< 0.1%
1813 1
< 0.1%
1816 1
< 0.1%
1819 1
< 0.1%
ValueCountFrequency (%)
2024 486
0.1%
2023 207
< 0.1%
2022 166
 
< 0.1%
2021 220
< 0.1%
2020 23
 
< 0.1%

month
Real number (ℝ)

Missing 

Distinct12
Distinct (%)< 0.1%
Missing82757
Missing (%)18.2%
Infinite0
Infinite (%)0.0%
Mean6.48793277
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:10.166782image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q39
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.079347836
Coefficient of variation (CV)0.4746269644
Kurtosis-1.001492335
Mean6.48793277
Median Absolute Deviation (MAD)2
Skewness0.04613171719
Sum2416463
Variance9.482383097
MonotonicityNot monotonic
2025-01-02T17:09:10.217191image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 47294
10.4%
6 37778
8.3%
9 37734
8.3%
8 35582
7.8%
4 34250
7.5%
7 33265
7.3%
3 32600
 
7.2%
11 31410
 
6.9%
10 23798
 
5.2%
2 22626
 
5.0%
Other values (2) 36118
7.9%
(Missing) 82757
18.2%
ValueCountFrequency (%)
1 18250
 
4.0%
2 22626
5.0%
3 32600
7.2%
4 34250
7.5%
5 47294
10.4%
ValueCountFrequency (%)
12 17868
3.9%
11 31410
6.9%
10 23798
5.2%
9 37734
8.3%
8 35582
7.8%

day
Unsupported

Missing  Rejected  Unsupported 

Missing108703
Missing (%)23.9%
Memory size3.5 MiB

verbatimEventDate
Text

Missing 

Distinct34047
Distinct (%)9.4%
Missing92472
Missing (%)20.3%
Memory size3.5 MiB
2025-01-02T17:09:10.381456image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length102
Median length98
Mean length26.64553123
Min length2

Characters and Unicode

Total characters9665400
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10960 ?
Unique (%)3.0%

Sample

1st row0000 00 00 - 0000 00 00
2nd row1938 Mar 25 - 0000 00 00
3rd row0000 00 00 - 0000 00 00
4th row1956 May 30 - 0000 00 00
5th row0000 00 00 - 0000 00 00
ValueCountFrequency (%)
00 796795
29.5%
420492
15.6%
0000 373796
13.9%
may 36088
 
1.3%
jun 32030
 
1.2%
sep 31699
 
1.2%
aug 30346
 
1.1%
apr 29080
 
1.1%
jul 27007
 
1.0%
mar 26144
 
1.0%
Other values (2486) 893609
33.1%
2025-01-02T17:09:10.637239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3533769
36.6%
2334346
24.2%
1 644236
 
6.7%
9 432298
 
4.5%
- 426014
 
4.4%
2 223234
 
2.3%
: 175106
 
1.8%
8 153818
 
1.6%
3 151315
 
1.6%
5 144564
 
1.5%
Other values (61) 1446700
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9665400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3533769
36.6%
2334346
24.2%
1 644236
 
6.7%
9 432298
 
4.5%
- 426014
 
4.4%
2 223234
 
2.3%
: 175106
 
1.8%
8 153818
 
1.6%
3 151315
 
1.6%
5 144564
 
1.5%
Other values (61) 1446700
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9665400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3533769
36.6%
2334346
24.2%
1 644236
 
6.7%
9 432298
 
4.5%
- 426014
 
4.4%
2 223234
 
2.3%
: 175106
 
1.8%
8 153818
 
1.6%
3 151315
 
1.6%
5 144564
 
1.5%
Other values (61) 1446700
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9665400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3533769
36.6%
2334346
24.2%
1 644236
 
6.7%
9 432298
 
4.5%
- 426014
 
4.4%
2 223234
 
2.3%
: 175106
 
1.8%
8 153818
 
1.6%
3 151315
 
1.6%
5 144564
 
1.5%
Other values (61) 1446700
15.0%

habitat
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

samplingProtocol
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

sampleSizeValue
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

sampleSizeUnit
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

samplingEffort
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

fieldNotes
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

eventRemarks
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

locationID
Text

Missing 

Distinct16404
Distinct (%)15.9%
Missing352012
Missing (%)77.3%
Memory size3.5 MiB
2025-01-02T17:09:10.796438image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length68
Median length40
Mean length5.144757752
Min length1

Characters and Unicode

Total characters530939
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6089 ?
Unique (%)5.9%

Sample

1st rowM10-97B4 (4
2nd row4-31N
3rd row5627
4th row308
5th rowB12 TR4
ValueCountFrequency (%)
d 13062
 
9.8%
tc 3543
 
2.7%
haul 1244
 
0.9%
trans 1038
 
0.8%
1 918
 
0.7%
2 894
 
0.7%
tt 799
 
0.6%
4 661
 
0.5%
3 655
 
0.5%
5 629
 
0.5%
Other values (13796) 109250
82.3%
2025-01-02T17:09:11.034897image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 61871
 
11.7%
2 49289
 
9.3%
- 37677
 
7.1%
3 36373
 
6.9%
4 36203
 
6.8%
5 34152
 
6.4%
0 31687
 
6.0%
29493
 
5.6%
7 29220
 
5.5%
6 27484
 
5.2%
Other values (65) 157490
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 530939
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 61871
 
11.7%
2 49289
 
9.3%
- 37677
 
7.1%
3 36373
 
6.9%
4 36203
 
6.8%
5 34152
 
6.4%
0 31687
 
6.0%
29493
 
5.6%
7 29220
 
5.5%
6 27484
 
5.2%
Other values (65) 157490
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 530939
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 61871
 
11.7%
2 49289
 
9.3%
- 37677
 
7.1%
3 36373
 
6.9%
4 36203
 
6.8%
5 34152
 
6.4%
0 31687
 
6.0%
29493
 
5.6%
7 29220
 
5.5%
6 27484
 
5.2%
Other values (65) 157490
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 530939
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 61871
 
11.7%
2 49289
 
9.3%
- 37677
 
7.1%
3 36373
 
6.9%
4 36203
 
6.8%
5 34152
 
6.4%
0 31687
 
6.0%
29493
 
5.6%
7 29220
 
5.5%
6 27484
 
5.2%
Other values (65) 157490
29.7%

higherGeographyID
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

higherGeography
Text

Missing 

Distinct13756
Distinct (%)3.2%
Missing20492
Missing (%)4.5%
Memory size3.5 MiB
2025-01-02T17:09:11.160463image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length177
Median length131
Mean length59.33840633
Min length4

Characters and Unicode

Total characters25795592
Distinct characters123
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3844 ?
Unique (%)0.9%

Sample

1st rowNorth Pacific Ocean, United States, Hawaii, Hawaiian Islands
2nd rowNorth Atlantic Ocean, Gulf of Mexico, United States, Florida, Hillsborough County
3rd rowNorth Pacific Ocean, Japan, Tokyo Prefecture, Japanese Archipelago, Honshu
4th rowNorth America, United States, West Virginia, Randolph County
5th rowAtlantic, Caribbean Sea, Barbados, Lesser Antilles, Barbados
ValueCountFrequency (%)
ocean 297628
 
8.7%
north 281556
 
8.2%
pacific 178084
 
5.2%
united 125556
 
3.7%
states 125313
 
3.7%
islands 124596
 
3.6%
atlantic 113798
 
3.3%
south 106956
 
3.1%
america 96813
 
2.8%
county 72814
 
2.1%
Other values (6594) 1896684
55.5%
2025-01-02T17:09:11.366808image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2985078
 
11.6%
a 2677647
 
10.4%
i 1842529
 
7.1%
n 1669001
 
6.5%
e 1623420
 
6.3%
t 1398934
 
5.4%
, 1342963
 
5.2%
o 1188502
 
4.6%
c 1128651
 
4.4%
r 1085797
 
4.2%
Other values (113) 8853070
34.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25795592
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2985078
 
11.6%
a 2677647
 
10.4%
i 1842529
 
7.1%
n 1669001
 
6.5%
e 1623420
 
6.3%
t 1398934
 
5.4%
, 1342963
 
5.2%
o 1188502
 
4.6%
c 1128651
 
4.4%
r 1085797
 
4.2%
Other values (113) 8853070
34.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25795592
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2985078
 
11.6%
a 2677647
 
10.4%
i 1842529
 
7.1%
n 1669001
 
6.5%
e 1623420
 
6.3%
t 1398934
 
5.4%
, 1342963
 
5.2%
o 1188502
 
4.6%
c 1128651
 
4.4%
r 1085797
 
4.2%
Other values (113) 8853070
34.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25795592
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2985078
 
11.6%
a 2677647
 
10.4%
i 1842529
 
7.1%
n 1669001
 
6.5%
e 1623420
 
6.3%
t 1398934
 
5.4%
, 1342963
 
5.2%
o 1188502
 
4.6%
c 1128651
 
4.4%
r 1085797
 
4.2%
Other values (113) 8853070
34.3%

continent
Text

Missing 

Distinct7
Distinct (%)< 0.1%
Missing162647
Missing (%)35.7%
Memory size3.5 MiB
2025-01-02T17:09:11.434182image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length10
Mean length8.959157794
Min length4

Characters and Unicode

Total characters2621136
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORTH_AMERICA
2nd rowNORTH_AMERICA
3rd rowASIA
4th rowAFRICA
5th rowOCEANIA
ValueCountFrequency (%)
north_america 101424
34.7%
asia 73673
25.2%
oceania 62827
21.5%
south_america 34099
 
11.7%
africa 17795
 
6.1%
europe 2346
 
0.8%
antarctica 401
 
0.1%
2025-01-02T17:09:11.554194image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 580839
22.2%
I 290219
11.1%
R 257489
9.8%
C 216947
 
8.3%
E 203042
 
7.7%
O 200696
 
7.7%
N 164652
 
6.3%
T 136325
 
5.2%
H 135523
 
5.2%
_ 135523
 
5.2%
Other values (5) 299881
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2621136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 580839
22.2%
I 290219
11.1%
R 257489
9.8%
C 216947
 
8.3%
E 203042
 
7.7%
O 200696
 
7.7%
N 164652
 
6.3%
T 136325
 
5.2%
H 135523
 
5.2%
_ 135523
 
5.2%
Other values (5) 299881
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2621136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 580839
22.2%
I 290219
11.1%
R 257489
9.8%
C 216947
 
8.3%
E 203042
 
7.7%
O 200696
 
7.7%
N 164652
 
6.3%
T 136325
 
5.2%
H 135523
 
5.2%
_ 135523
 
5.2%
Other values (5) 299881
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2621136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 580839
22.2%
I 290219
11.1%
R 257489
9.8%
C 216947
 
8.3%
E 203042
 
7.7%
O 200696
 
7.7%
N 164652
 
6.3%
T 136325
 
5.2%
H 135523
 
5.2%
_ 135523
 
5.2%
Other values (5) 299881
11.4%

waterBody
Text

Missing 

Distinct1776
Distinct (%)0.6%
Missing133275
Missing (%)29.3%
Memory size3.5 MiB
2025-01-02T17:09:11.709385image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length72
Median length71
Mean length24.05968559
Min length6

Characters and Unicode

Total characters7745703
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique489 ?
Unique (%)0.2%

Sample

1st rowNorth Pacific Ocean
2nd rowNorth Atlantic Ocean, Gulf of Mexico
3rd rowNorth Pacific Ocean
4th rowAtlantic, Caribbean Sea
5th rowNorth Pacific Ocean
ValueCountFrequency (%)
ocean 296315
24.5%
north 200693
16.6%
pacific 178071
14.7%
atlantic 113701
 
9.4%
south 68065
 
5.6%
sea 63584
 
5.3%
of 34822
 
2.9%
gulf 34750
 
2.9%
bay 30113
 
2.5%
indian 28800
 
2.4%
Other values (1364) 159134
13.2%
2025-01-02T17:09:11.952932image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
886111
11.4%
a 885823
11.4%
c 796020
 
10.3%
i 598543
 
7.7%
n 555364
 
7.2%
t 522462
 
6.7%
e 465166
 
6.0%
o 359377
 
4.6%
O 297236
 
3.8%
h 288188
 
3.7%
Other values (58) 2091413
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7745703
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
886111
11.4%
a 885823
11.4%
c 796020
 
10.3%
i 598543
 
7.7%
n 555364
 
7.2%
t 522462
 
6.7%
e 465166
 
6.0%
o 359377
 
4.6%
O 297236
 
3.8%
h 288188
 
3.7%
Other values (58) 2091413
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7745703
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
886111
11.4%
a 885823
11.4%
c 796020
 
10.3%
i 598543
 
7.7%
n 555364
 
7.2%
t 522462
 
6.7%
e 465166
 
6.0%
o 359377
 
4.6%
O 297236
 
3.8%
h 288188
 
3.7%
Other values (58) 2091413
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7745703
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
886111
11.4%
a 885823
11.4%
c 796020
 
10.3%
i 598543
 
7.7%
n 555364
 
7.2%
t 522462
 
6.7%
e 465166
 
6.0%
o 359377
 
4.6%
O 297236
 
3.8%
h 288188
 
3.7%
Other values (58) 2091413
27.0%

islandGroup
Text

Missing 

Distinct323
Distinct (%)0.5%
Missing390811
Missing (%)85.9%
Memory size3.5 MiB
2025-01-02T17:09:12.207054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length45
Median length32
Mean length14.81478548
Min length4

Characters and Unicode

Total characters954087
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)0.1%

Sample

1st rowFlorida Islands
2nd rowVava'u Group
3rd rowVisayas
4th rowCuyo Islands
5th rowHa'apai Group
ValueCountFrequency (%)
islands 31617
22.3%
group 13997
 
9.9%
chain 5485
 
3.9%
visayas 4942
 
3.5%
leeward 4824
 
3.4%
ralik 4613
 
3.3%
bahama 2866
 
2.0%
island 2805
 
2.0%
cruz 2205
 
1.6%
santa 2205
 
1.6%
Other values (354) 66278
46.7%
2025-01-02T17:09:12.422153image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 143525
15.0%
s 92363
 
9.7%
77436
 
8.1%
n 71038
 
7.4%
l 57390
 
6.0%
d 51466
 
5.4%
r 46473
 
4.9%
u 38564
 
4.0%
o 37605
 
3.9%
i 37528
 
3.9%
Other values (54) 300699
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 954087
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 143525
15.0%
s 92363
 
9.7%
77436
 
8.1%
n 71038
 
7.4%
l 57390
 
6.0%
d 51466
 
5.4%
r 46473
 
4.9%
u 38564
 
4.0%
o 37605
 
3.9%
i 37528
 
3.9%
Other values (54) 300699
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 954087
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 143525
15.0%
s 92363
 
9.7%
77436
 
8.1%
n 71038
 
7.4%
l 57390
 
6.0%
d 51466
 
5.4%
r 46473
 
4.9%
u 38564
 
4.0%
o 37605
 
3.9%
i 37528
 
3.9%
Other values (54) 300699
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 954087
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 143525
15.0%
s 92363
 
9.7%
77436
 
8.1%
n 71038
 
7.4%
l 57390
 
6.0%
d 51466
 
5.4%
r 46473
 
4.9%
u 38564
 
4.0%
o 37605
 
3.9%
i 37528
 
3.9%
Other values (54) 300699
31.5%

island
Text

Missing 

Distinct2224
Distinct (%)1.2%
Missing270596
Missing (%)59.4%
Memory size3.5 MiB
2025-01-02T17:09:12.568108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length43
Median length37
Mean length9.782494475
Min length3

Characters and Unicode

Total characters1806005
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique463 ?
Unique (%)0.3%

Sample

1st rowHonshu
2nd rowBarbados
3rd rowPutic Island
4th rowGuam
5th rowFlorida Island
ValueCountFrequency (%)
island 45621
 
15.8%
bermuda 14507
 
5.0%
atoll 13109
 
4.5%
luzon 7631
 
2.6%
oahu 6792
 
2.3%
cay 5201
 
1.8%
bow 3799
 
1.3%
carrie 3799
 
1.3%
new 3013
 
1.0%
cuba 2705
 
0.9%
Other values (2080) 182972
63.3%
2025-01-02T17:09:12.785078image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 268860
14.9%
n 133966
 
7.4%
o 116663
 
6.5%
l 110963
 
6.1%
104533
 
5.8%
u 90794
 
5.0%
e 88610
 
4.9%
i 86645
 
4.8%
r 86277
 
4.8%
d 84321
 
4.7%
Other values (70) 634373
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1806005
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 268860
14.9%
n 133966
 
7.4%
o 116663
 
6.5%
l 110963
 
6.1%
104533
 
5.8%
u 90794
 
5.0%
e 88610
 
4.9%
i 86645
 
4.8%
r 86277
 
4.8%
d 84321
 
4.7%
Other values (70) 634373
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1806005
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 268860
14.9%
n 133966
 
7.4%
o 116663
 
6.5%
l 110963
 
6.1%
104533
 
5.8%
u 90794
 
5.0%
e 88610
 
4.9%
i 86645
 
4.8%
r 86277
 
4.8%
d 84321
 
4.7%
Other values (70) 634373
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1806005
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 268860
14.9%
n 133966
 
7.4%
o 116663
 
6.5%
l 110963
 
6.1%
104533
 
5.8%
u 90794
 
5.0%
e 88610
 
4.9%
i 86645
 
4.8%
r 86277
 
4.8%
d 84321
 
4.7%
Other values (70) 634373
35.1%

countryCode
Text

Missing 

Distinct217
Distinct (%)0.1%
Missing30434
Missing (%)6.7%
Memory size3.5 MiB
2025-01-02T17:09:12.952373image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters849556
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowUS
2nd rowUS
3rd rowJP
4th rowUS
5th rowBB
ValueCountFrequency (%)
us 124564
29.3%
ph 46190
 
10.9%
bm 15821
 
3.7%
id 12805
 
3.0%
br 11602
 
2.7%
pa 10456
 
2.5%
pf 9998
 
2.4%
pg 7692
 
1.8%
jp 7188
 
1.7%
au 7086
 
1.7%
Other values (207) 171376
40.3%
2025-01-02T17:09:13.174602image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 147534
17.4%
S 146438
17.2%
P 91776
10.8%
H 58467
 
6.9%
B 50362
 
5.9%
M 50159
 
5.9%
C 28118
 
3.3%
A 26140
 
3.1%
I 23175
 
2.7%
T 22845
 
2.7%
Other values (16) 204542
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 849556
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 147534
17.4%
S 146438
17.2%
P 91776
10.8%
H 58467
 
6.9%
B 50362
 
5.9%
M 50159
 
5.9%
C 28118
 
3.3%
A 26140
 
3.1%
I 23175
 
2.7%
T 22845
 
2.7%
Other values (16) 204542
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 849556
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 147534
17.4%
S 146438
17.2%
P 91776
10.8%
H 58467
 
6.9%
B 50362
 
5.9%
M 50159
 
5.9%
C 28118
 
3.3%
A 26140
 
3.1%
I 23175
 
2.7%
T 22845
 
2.7%
Other values (16) 204542
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 849556
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 147534
17.4%
S 146438
17.2%
P 91776
10.8%
H 58467
 
6.9%
B 50362
 
5.9%
M 50159
 
5.9%
C 28118
 
3.3%
A 26140
 
3.1%
I 23175
 
2.7%
T 22845
 
2.7%
Other values (16) 204542
24.1%

stateProvince
Text

Missing 

Distinct1486
Distinct (%)0.5%
Missing174301
Missing (%)38.3%
Memory size3.5 MiB
2025-01-02T17:09:13.337304image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length48
Median length36
Mean length11.08342144
Min length3

Characters and Unicode

Total characters3113455
Distinct characters97
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique252 ?
Unique (%)0.1%

Sample

1st rowHawaii
2nd rowFlorida
3rd rowTokyo Prefecture
4th rowWest Virginia
5th rowPalawan
ValueCountFrequency (%)
province 30907
 
7.1%
florida 17201
 
4.0%
carolina 12504
 
2.9%
virginia 11495
 
2.7%
hawaii 10718
 
2.5%
north 9674
 
2.2%
region 9360
 
2.2%
south 8306
 
1.9%
maryland 7690
 
1.8%
islands 6712
 
1.6%
Other values (1479) 308426
71.2%
2025-01-02T17:09:13.569251image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 411155
13.2%
i 266017
 
8.5%
n 234755
 
7.5%
o 224195
 
7.2%
e 216314
 
6.9%
r 215084
 
6.9%
152082
 
4.9%
s 136931
 
4.4%
t 132525
 
4.3%
l 123796
 
4.0%
Other values (87) 1000601
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3113455
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 411155
13.2%
i 266017
 
8.5%
n 234755
 
7.5%
o 224195
 
7.2%
e 216314
 
6.9%
r 215084
 
6.9%
152082
 
4.9%
s 136931
 
4.4%
t 132525
 
4.3%
l 123796
 
4.0%
Other values (87) 1000601
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3113455
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 411155
13.2%
i 266017
 
8.5%
n 234755
 
7.5%
o 224195
 
7.2%
e 216314
 
6.9%
r 215084
 
6.9%
152082
 
4.9%
s 136931
 
4.4%
t 132525
 
4.3%
l 123796
 
4.0%
Other values (87) 1000601
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3113455
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 411155
13.2%
i 266017
 
8.5%
n 234755
 
7.5%
o 224195
 
7.2%
e 216314
 
6.9%
r 215084
 
6.9%
152082
 
4.9%
s 136931
 
4.4%
t 132525
 
4.3%
l 123796
 
4.0%
Other values (87) 1000601
32.1%

county
Text

Missing 

Distinct2317
Distinct (%)2.4%
Missing357533
Missing (%)78.5%
Memory size3.5 MiB
2025-01-02T17:09:13.734207image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length46
Median length40
Mean length14.85270119
Min length3

Characters and Unicode

Total characters1450797
Distinct characters87
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique418 ?
Unique (%)0.4%

Sample

1st rowHillsborough County
2nd rowRandolph County
3rd rowThoothukudi District
4th rowCalvert County
5th rowNew Hanover County
ValueCountFrequency (%)
county 71646
34.9%
district 9126
 
4.4%
honolulu 5828
 
2.8%
monroe 3066
 
1.5%
parish 2197
 
1.1%
carteret 1943
 
0.9%
borough 1790
 
0.9%
san 1543
 
0.8%
montgomery 1350
 
0.7%
barnstable 1256
 
0.6%
Other values (2386) 105465
51.4%
2025-01-02T17:09:13.974142image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 144007
 
9.9%
o 142669
 
9.8%
t 126035
 
8.7%
u 110453
 
7.6%
107531
 
7.4%
a 92081
 
6.3%
C 86847
 
6.0%
y 83560
 
5.8%
e 75882
 
5.2%
r 67041
 
4.6%
Other values (77) 414691
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1450797
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 144007
 
9.9%
o 142669
 
9.8%
t 126035
 
8.7%
u 110453
 
7.6%
107531
 
7.4%
a 92081
 
6.3%
C 86847
 
6.0%
y 83560
 
5.8%
e 75882
 
5.2%
r 67041
 
4.6%
Other values (77) 414691
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1450797
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 144007
 
9.9%
o 142669
 
9.8%
t 126035
 
8.7%
u 110453
 
7.6%
107531
 
7.4%
a 92081
 
6.3%
C 86847
 
6.0%
y 83560
 
5.8%
e 75882
 
5.2%
r 67041
 
4.6%
Other values (77) 414691
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1450797
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 144007
 
9.9%
o 142669
 
9.8%
t 126035
 
8.7%
u 110453
 
7.6%
107531
 
7.4%
a 92081
 
6.3%
C 86847
 
6.0%
y 83560
 
5.8%
e 75882
 
5.2%
r 67041
 
4.6%
Other values (77) 414691
28.6%

municipality
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

locality
Text

Missing 

Distinct63948
Distinct (%)15.6%
Missing45084
Missing (%)9.9%
Memory size3.5 MiB
2025-01-02T17:09:14.125206image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length653
Median length273
Mean length54.14088285
Min length1

Characters and Unicode

Total characters22204692
Distinct characters113
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31251 ?
Unique (%)7.6%

Sample

1st rowHawaii
2nd rowTampa, Florida
3rd rowTokyo, Japan
4th rowWest Virginia, Randolph County, Shaver's Fork at Cheat Bridge on US Route 250 (Durbin Quad)
5th rowNo Data
ValueCountFrequency (%)
of 176506
 
5.1%
island 102327
 
3.0%
islands 48966
 
1.4%
bay 45484
 
1.3%
river 43645
 
1.3%
reef 43102
 
1.2%
off 42172
 
1.2%
and 41056
 
1.2%
at 38682
 
1.1%
south 38327
 
1.1%
Other values (37098) 2831018
82.0%
2025-01-02T17:09:14.357276image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3041158
 
13.7%
a 2147970
 
9.7%
e 1545770
 
7.0%
o 1444005
 
6.5%
n 1302074
 
5.9%
i 1144156
 
5.2%
t 1048248
 
4.7%
r 1047962
 
4.7%
s 988467
 
4.5%
l 837964
 
3.8%
Other values (103) 7656918
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22204692
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3041158
 
13.7%
a 2147970
 
9.7%
e 1545770
 
7.0%
o 1444005
 
6.5%
n 1302074
 
5.9%
i 1144156
 
5.2%
t 1048248
 
4.7%
r 1047962
 
4.7%
s 988467
 
4.5%
l 837964
 
3.8%
Other values (103) 7656918
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22204692
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3041158
 
13.7%
a 2147970
 
9.7%
e 1545770
 
7.0%
o 1444005
 
6.5%
n 1302074
 
5.9%
i 1144156
 
5.2%
t 1048248
 
4.7%
r 1047962
 
4.7%
s 988467
 
4.5%
l 837964
 
3.8%
Other values (103) 7656918
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22204692
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3041158
 
13.7%
a 2147970
 
9.7%
e 1545770
 
7.0%
o 1444005
 
6.5%
n 1302074
 
5.9%
i 1144156
 
5.2%
t 1048248
 
4.7%
r 1047962
 
4.7%
s 988467
 
4.5%
l 837964
 
3.8%
Other values (103) 7656918
34.5%

verbatimLocality
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

verbatimElevation
Text

Missing 

Distinct76
Distinct (%)3.4%
Missing453008
Missing (%)99.5%
Memory size3.5 MiB
2025-01-02T17:09:14.499804image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length152
Median length68
Mean length46.38838475
Min length3

Characters and Unicode

Total characters102240
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.6%

Sample

1st rowRotenone put out at 90' and 120', pickup was surface to 140', several (fiscos=factors?) prevented an even better collection.
2nd rowDistance from shore: 1000 feet
3rd row32 not found in field notes so could be inaccurate.
4th rowDistance from shore: 1500 feet
5th rowNaso was speared by P.W. (Paul D. West)
ValueCountFrequency (%)
feet 1680
 
9.0%
distance 1141
 
6.1%
from 1097
 
5.9%
to 1064
 
5.7%
shore 1048
 
5.6%
at 595
 
3.2%
499
 
2.7%
and 445
 
2.4%
rotenone 430
 
2.3%
put 309
 
1.6%
Other values (175) 10428
55.7%
2025-01-02T17:09:14.720464image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16532
16.2%
e 10266
 
10.0%
t 7363
 
7.2%
o 6811
 
6.7%
a 5065
 
5.0%
s 4792
 
4.7%
f 4205
 
4.1%
n 4144
 
4.1%
r 4103
 
4.0%
0 3353
 
3.3%
Other values (60) 35606
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 102240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
16532
16.2%
e 10266
 
10.0%
t 7363
 
7.2%
o 6811
 
6.7%
a 5065
 
5.0%
s 4792
 
4.7%
f 4205
 
4.1%
n 4144
 
4.1%
r 4103
 
4.0%
0 3353
 
3.3%
Other values (60) 35606
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 102240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
16532
16.2%
e 10266
 
10.0%
t 7363
 
7.2%
o 6811
 
6.7%
a 5065
 
5.0%
s 4792
 
4.7%
f 4205
 
4.1%
n 4144
 
4.1%
r 4103
 
4.0%
0 3353
 
3.3%
Other values (60) 35606
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 102240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
16532
16.2%
e 10266
 
10.0%
t 7363
 
7.2%
o 6811
 
6.7%
a 5065
 
5.0%
s 4792
 
4.7%
f 4205
 
4.1%
n 4144
 
4.1%
r 4103
 
4.0%
0 3353
 
3.3%
Other values (60) 35606
34.8%

verticalDatum
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

verbatimDepth
Text

Missing 

Distinct230
Distinct (%)2.7%
Missing446636
Missing (%)98.1%
Memory size3.5 MiB
2025-01-02T17:09:14.883900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length72
Median length67
Mean length8.249766791
Min length1

Characters and Unicode

Total characters70750
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)1.1%

Sample

1st rowDepth trawl: 135 fathoms
2nd row15 minutes at depth
3rd rowSurface
4th rowCA
5th row15 minutes at depth
ValueCountFrequency (%)
ca 3930
25.9%
surface 2351
15.5%
depth 865
 
5.7%
at 571
 
3.8%
00000000 543
 
3.6%
to 505
 
3.3%
minutes 343
 
2.3%
fathoms 330
 
2.2%
m 320
 
2.1%
trawl 287
 
1.9%
Other values (305) 5114
33.7%
2025-01-02T17:09:15.118453image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7671
 
10.8%
6583
 
9.3%
e 5141
 
7.3%
a 4475
 
6.3%
t 4124
 
5.8%
A 4103
 
5.8%
C 3949
 
5.6%
r 3468
 
4.9%
f 3202
 
4.5%
u 3133
 
4.4%
Other values (66) 24901
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7671
 
10.8%
6583
 
9.3%
e 5141
 
7.3%
a 4475
 
6.3%
t 4124
 
5.8%
A 4103
 
5.8%
C 3949
 
5.6%
r 3468
 
4.9%
f 3202
 
4.5%
u 3133
 
4.4%
Other values (66) 24901
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7671
 
10.8%
6583
 
9.3%
e 5141
 
7.3%
a 4475
 
6.3%
t 4124
 
5.8%
A 4103
 
5.8%
C 3949
 
5.6%
r 3468
 
4.9%
f 3202
 
4.5%
u 3133
 
4.4%
Other values (66) 24901
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7671
 
10.8%
6583
 
9.3%
e 5141
 
7.3%
a 4475
 
6.3%
t 4124
 
5.8%
A 4103
 
5.8%
C 3949
 
5.6%
r 3468
 
4.9%
f 3202
 
4.5%
u 3133
 
4.4%
Other values (66) 24901
35.2%

minimumDistanceAboveSurfaceInMeters
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing455211
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:15.183157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters20
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowWilliams, Jeffrey T.
ValueCountFrequency (%)
williams 1
33.3%
jeffrey 1
33.3%
t 1
33.3%
2025-01-02T17:09:15.292240image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 2
 
10.0%
l 2
 
10.0%
2
 
10.0%
e 2
 
10.0%
f 2
 
10.0%
a 1
 
5.0%
W 1
 
5.0%
, 1
 
5.0%
s 1
 
5.0%
m 1
 
5.0%
Other values (5) 5
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 2
 
10.0%
l 2
 
10.0%
2
 
10.0%
e 2
 
10.0%
f 2
 
10.0%
a 1
 
5.0%
W 1
 
5.0%
, 1
 
5.0%
s 1
 
5.0%
m 1
 
5.0%
Other values (5) 5
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 2
 
10.0%
l 2
 
10.0%
2
 
10.0%
e 2
 
10.0%
f 2
 
10.0%
a 1
 
5.0%
W 1
 
5.0%
, 1
 
5.0%
s 1
 
5.0%
m 1
 
5.0%
Other values (5) 5
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 2
 
10.0%
l 2
 
10.0%
2
 
10.0%
e 2
 
10.0%
f 2
 
10.0%
a 1
 
5.0%
W 1
 
5.0%
, 1
 
5.0%
s 1
 
5.0%
m 1
 
5.0%
Other values (5) 5
25.0%

maximumDistanceAboveSurfaceInMeters
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

locationAccordingTo
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

locationRemarks
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

decimalLatitude
Real number (ℝ)

Missing 

Distinct15632
Distinct (%)7.8%
Missing254257
Missing (%)55.9%
Infinite0
Infinite (%)0.0%
Mean11.69417692
Minimum-84.18
Maximum80.167
Zeros45
Zeros (%)< 0.1%
Negative57724
Negative (%)12.7%
Memory size3.5 MiB
2025-01-02T17:09:15.351187image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-84.18
5-th percentile-21.1342
Q1-3.7633
median12.1
Q329.0417
95-th percentile40.45
Maximum80.167
Range164.347
Interquartile range (IQR)32.805

Descriptive statistics

Standard deviation20.80997853
Coefficient of variation (CV)1.779516307
Kurtosis-0.09104390881
Mean11.69417692
Median Absolute Deviation (MAD)16.566
Skewness-0.401774674
Sum2350003.323
Variance433.0552063
MonotonicityNot monotonic
2025-01-02T17:09:15.413117image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-12.5 1195
 
0.3%
27.9 868
 
0.2%
16.8 711
 
0.2%
12.0832 620
 
0.1%
21.417 545
 
0.1%
-19.1606 541
 
0.1%
32.23 497
 
0.1%
32.17 493
 
0.1%
32.3 491
 
0.1%
28.4933 489
 
0.1%
Other values (15622) 194505
42.7%
(Missing) 254257
55.9%
ValueCountFrequency (%)
-84.18 1
< 0.1%
-78.2892 1
< 0.1%
-78.27 1
< 0.1%
-78.158 2
< 0.1%
-77.975 1
< 0.1%
ValueCountFrequency (%)
80.167 7
< 0.1%
80.158 1
 
< 0.1%
80.058 1
 
< 0.1%
79.483 2
 
< 0.1%
79.33 3
< 0.1%

decimalLongitude
Real number (ℝ)

Missing 

Distinct17148
Distinct (%)8.5%
Missing254257
Missing (%)55.9%
Infinite0
Infinite (%)0.0%
Mean-14.76746133
Minimum-180
Maximum179.93
Zeros20
Zeros (%)< 0.1%
Negative125692
Negative (%)27.6%
Memory size3.5 MiB
2025-01-02T17:09:15.475121image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-180
5-th percentile-158.48
Q1-82.925
median-64
Q3113.83
95-th percentile166.292
Maximum179.93
Range359.93
Interquartile range (IQR)196.755

Descriptive statistics

Standard deviation106.956974
Coefficient of variation (CV)-7.242746168
Kurtosis-1.227338423
Mean-14.76746133
Median Absolute Deviation (MAD)65.25
Skewness0.43177466
Sum-2967595.192
Variance11439.79428
MonotonicityNot monotonic
2025-01-02T17:09:15.538642image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177.083 872
 
0.2%
-93.717 815
 
0.2%
-88.08 737
 
0.2%
-68.8991 618
 
0.1%
-64 561
 
0.1%
-158.417 546
 
0.1%
179.756 541
 
0.1%
162.875 490
 
0.1%
165.83 469
 
0.1%
-84.9317 454
 
0.1%
Other values (17138) 194852
42.8%
(Missing) 254257
55.9%
ValueCountFrequency (%)
-180 2
 
< 0.1%
-179.95 20
< 0.1%
-179.946 1
 
< 0.1%
-179.933 1
 
< 0.1%
-179.909 2
 
< 0.1%
ValueCountFrequency (%)
179.93 4
 
< 0.1%
179.88 2
 
< 0.1%
179.87 10
< 0.1%
179.862 1
 
< 0.1%
179.82 22
< 0.1%

coordinateUncertaintyInMeters
Real number (ℝ)

Missing 

Distinct220
Distinct (%)4.3%
Missing450059
Missing (%)98.9%
Infinite0
Infinite (%)0.0%
Mean13782.04143
Minimum10
Maximum534670
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:15.606045image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile100
Q1116
median653
Q310000
95-th percentile50000
Maximum534670
Range534660
Interquartile range (IQR)9884

Descriptive statistics

Standard deviation46742.55849
Coefficient of variation (CV)3.391555506
Kurtosis31.54301765
Mean13782.04143
Median Absolute Deviation (MAD)603
Skewness5.444579332
Sum71018859.48
Variance2184866774
MonotonicityNot monotonic
2025-01-02T17:09:15.798266image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1109
 
0.2%
10000 832
 
0.2%
3704 209
 
< 0.1%
500 188
 
< 0.1%
5000 122
 
< 0.1%
278076 107
 
< 0.1%
441 89
 
< 0.1%
330 83
 
< 0.1%
50 78
 
< 0.1%
161 73
 
< 0.1%
Other values (210) 2263
 
0.5%
(Missing) 450059
98.9%
ValueCountFrequency (%)
10 3
 
< 0.1%
30 28
 
< 0.1%
40 44
< 0.1%
50 78
< 0.1%
75 1
 
< 0.1%
ValueCountFrequency (%)
534670 1
 
< 0.1%
477476 1
 
< 0.1%
435450 4
 
< 0.1%
291123 11
 
< 0.1%
278076 107
< 0.1%

coordinatePrecision
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

pointRadiusSpatialFit
Real number (ℝ)

Missing 

Distinct7
Distinct (%)100.0%
Missing455205
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean2370838.571
Minimum2335095
Maximum2414948
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:15.857350image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2335095
5-th percentile2336877.3
Q12347255.5
median2373066
Q32389125
95-th percentile2408598.2
Maximum2414948
Range79853
Interquartile range (IQR)41869.5

Descriptive statistics

Standard deviation29240.54018
Coefficient of variation (CV)0.01233341676
Kurtosis-1.180951489
Mean2370838.571
Median Absolute Deviation (MAD)20716
Skewness0.2153112136
Sum16595870
Variance855009190
MonotonicityNot monotonic
2025-01-02T17:09:15.916689image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2341036 1
 
< 0.1%
2384468 1
 
< 0.1%
2353475 1
 
< 0.1%
2373066 1
 
< 0.1%
2414948 1
 
< 0.1%
2393782 1
 
< 0.1%
2335095 1
 
< 0.1%
(Missing) 455205
> 99.9%
ValueCountFrequency (%)
2335095 1
< 0.1%
2341036 1
< 0.1%
2353475 1
< 0.1%
2373066 1
< 0.1%
2384468 1
< 0.1%
ValueCountFrequency (%)
2414948 1
< 0.1%
2393782 1
< 0.1%
2384468 1
< 0.1%
2373066 1
< 0.1%
2353475 1
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing308939
Missing (%)67.9%
Memory size3.5 MiB
2025-01-02T17:09:15.982456image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length23
Mean length22.92758746
Min length7

Characters and Unicode

Total characters3353687
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDegrees Minutes Seconds
2nd rowDegrees Minutes Seconds
3rd rowDegrees Minutes Seconds
4th rowDegrees Minutes Seconds
5th rowDegrees Minutes Seconds
ValueCountFrequency (%)
degrees 146265
33.4%
minutes 144957
33.1%
seconds 144957
33.1%
decimal 1308
 
0.3%
unknown 8
 
< 0.1%
2025-01-02T17:09:16.111248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 730017
21.8%
s 436179
13.0%
291222
 
8.7%
n 289938
 
8.6%
D 146265
 
4.4%
r 146265
 
4.4%
g 146265
 
4.4%
i 146265
 
4.4%
c 146265
 
4.4%
d 146265
 
4.4%
Other values (11) 728741
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3353687
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 730017
21.8%
s 436179
13.0%
291222
 
8.7%
n 289938
 
8.6%
D 146265
 
4.4%
r 146265
 
4.4%
g 146265
 
4.4%
i 146265
 
4.4%
c 146265
 
4.4%
d 146265
 
4.4%
Other values (11) 728741
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3353687
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 730017
21.8%
s 436179
13.0%
291222
 
8.7%
n 289938
 
8.6%
D 146265
 
4.4%
r 146265
 
4.4%
g 146265
 
4.4%
i 146265
 
4.4%
c 146265
 
4.4%
d 146265
 
4.4%
Other values (11) 728741
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3353687
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 730017
21.8%
s 436179
13.0%
291222
 
8.7%
n 289938
 
8.6%
D 146265
 
4.4%
r 146265
 
4.4%
g 146265
 
4.4%
i 146265
 
4.4%
c 146265
 
4.4%
d 146265
 
4.4%
Other values (11) 728741
21.7%

verbatimSRS
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

footprintWKT
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

footprintSRS
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

footprintSpatialFit
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

georeferencedBy
Text

Missing 

Distinct7
Distinct (%)100.0%
Missing455205
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:16.190591image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length40
Median length36
Mean length35.85714286
Min length33

Characters and Unicode

Total characters251
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st rowNoturus nocturnus Jordan & Gilbert, 1886
2nd rowThalassoma lunare (Linnaeus, 1758)
3rd rowBrycon falcatus Müller & Troschel, 1844
4th rowPseudotropheus elongatus Fryer, 1956
5th rowHalieutaea brevicauda Ogilby, 1910
ValueCountFrequency (%)
2
 
6.2%
nocturnus 1
 
3.1%
noturus 1
 
3.1%
jordan 1
 
3.1%
gilbert 1
 
3.1%
1886 1
 
3.1%
thalassoma 1
 
3.1%
lunare 1
 
3.1%
linnaeus 1
 
3.1%
1758 1
 
3.1%
Other values (21) 21
65.6%
2025-01-02T17:09:16.342742image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
25
 
10.0%
a 19
 
7.6%
s 18
 
7.2%
e 17
 
6.8%
r 15
 
6.0%
l 15
 
6.0%
u 14
 
5.6%
n 11
 
4.4%
o 11
 
4.4%
i 9
 
3.6%
Other values (37) 97
38.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 251
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
25
 
10.0%
a 19
 
7.6%
s 18
 
7.2%
e 17
 
6.8%
r 15
 
6.0%
l 15
 
6.0%
u 14
 
5.6%
n 11
 
4.4%
o 11
 
4.4%
i 9
 
3.6%
Other values (37) 97
38.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 251
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
25
 
10.0%
a 19
 
7.6%
s 18
 
7.2%
e 17
 
6.8%
r 15
 
6.0%
l 15
 
6.0%
u 14
 
5.6%
n 11
 
4.4%
o 11
 
4.4%
i 9
 
3.6%
Other values (37) 97
38.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 251
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
25
 
10.0%
a 19
 
7.6%
s 18
 
7.2%
e 17
 
6.8%
r 15
 
6.0%
l 15
 
6.0%
u 14
 
5.6%
n 11
 
4.4%
o 11
 
4.4%
i 9
 
3.6%
Other values (37) 97
38.6%

georeferencedDate
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

georeferenceProtocol
Text

Missing 

Distinct16
Distinct (%)0.1%
Missing437832
Missing (%)96.2%
Memory size3.5 MiB
2025-01-02T17:09:16.424265image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length125
Median length96
Mean length19.25863061
Min length3

Characters and Unicode

Total characters334715
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGPS
2nd rowOn-line Gazetteer
3rd rowDifferential GPS
4th rowGuide to Best Practices for Georeferencing. (Chapman and Wieczorek, eds. 2006). Google Earth Pro
5th rowChart
ValueCountFrequency (%)
chart 6339
 
11.9%
gps 6318
 
11.9%
google 3627
 
6.8%
earth 3256
 
6.1%
georeferencing 2448
 
4.6%
and 2426
 
4.6%
guide 2399
 
4.5%
for 2399
 
4.5%
practices 2399
 
4.5%
best 2399
 
4.5%
Other values (37) 19229
36.1%
2025-01-02T17:09:16.566260image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35859
 
10.7%
e 34001
 
10.2%
r 26699
 
8.0%
a 21993
 
6.6%
t 20226
 
6.0%
o 19852
 
5.9%
G 16342
 
4.9%
n 13437
 
4.0%
h 12548
 
3.7%
i 12159
 
3.6%
Other values (51) 121599
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 334715
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
35859
 
10.7%
e 34001
 
10.2%
r 26699
 
8.0%
a 21993
 
6.6%
t 20226
 
6.0%
o 19852
 
5.9%
G 16342
 
4.9%
n 13437
 
4.0%
h 12548
 
3.7%
i 12159
 
3.6%
Other values (51) 121599
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 334715
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
35859
 
10.7%
e 34001
 
10.2%
r 26699
 
8.0%
a 21993
 
6.6%
t 20226
 
6.0%
o 19852
 
5.9%
G 16342
 
4.9%
n 13437
 
4.0%
h 12548
 
3.7%
i 12159
 
3.6%
Other values (51) 121599
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 334715
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
35859
 
10.7%
e 34001
 
10.2%
r 26699
 
8.0%
a 21993
 
6.6%
t 20226
 
6.0%
o 19852
 
5.9%
G 16342
 
4.9%
n 13437
 
4.0%
h 12548
 
3.7%
i 12159
 
3.6%
Other values (51) 121599
36.3%

georeferenceSources
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

georeferenceRemarks
Text

Missing 

Distinct135
Distinct (%)0.6%
Missing432197
Missing (%)94.9%
Memory size3.5 MiB
2025-01-02T17:09:16.694180image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length158
Median length2
Mean length7.226026504
Min length1

Characters and Unicode

Total characters166307
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)0.3%

Sample

1st rowStart; End
2nd rowca
3rd rowCA
4th rowCA
5th rowCA
ValueCountFrequency (%)
ca 18410
46.3%
start 2530
 
6.4%
end 2436
 
6.1%
bank 1768
 
4.4%
flower 1768
 
4.4%
garden 1768
 
4.4%
for 977
 
2.5%
west 940
 
2.4%
east 828
 
2.1%
coordinates 580
 
1.5%
Other values (263) 7789
19.6%
2025-01-02T17:09:16.906642image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 17102
 
10.3%
16779
 
10.1%
A 16547
 
9.9%
a 12099
 
7.3%
t 11571
 
7.0%
n 10340
 
6.2%
e 9905
 
6.0%
r 8710
 
5.2%
o 7884
 
4.7%
d 6230
 
3.7%
Other values (50) 49140
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 166307
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 17102
 
10.3%
16779
 
10.1%
A 16547
 
9.9%
a 12099
 
7.3%
t 11571
 
7.0%
n 10340
 
6.2%
e 9905
 
6.0%
r 8710
 
5.2%
o 7884
 
4.7%
d 6230
 
3.7%
Other values (50) 49140
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 166307
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 17102
 
10.3%
16779
 
10.1%
A 16547
 
9.9%
a 12099
 
7.3%
t 11571
 
7.0%
n 10340
 
6.2%
e 9905
 
6.0%
r 8710
 
5.2%
o 7884
 
4.7%
d 6230
 
3.7%
Other values (50) 49140
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 166307
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 17102
 
10.3%
16779
 
10.1%
A 16547
 
9.9%
a 12099
 
7.3%
t 11571
 
7.0%
n 10340
 
6.2%
e 9905
 
6.0%
r 8710
 
5.2%
o 7884
 
4.7%
d 6230
 
3.7%
Other values (50) 49140
29.5%

geologicalContextID
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

earliestEonOrLowestEonothem
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB
Distinct7
Distinct (%)100.0%
Missing455205
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:16.995733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length141
Median length134
Mean length126.7142857
Min length114

Characters and Unicode

Total characters887
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st rowAnimalia, Chordata, Vertebrata, Osteichthyes, Actinopterygii, Neopterygii, Ostariophysi, Siluriformes, Ictaluridae
2nd rowAnimalia, Chordata, Vertebrata, Osteichthyes, Actinopterygii, Neopterygii, Acanthopterygii, Perciformes, Labroidei, Labridae
3rd rowAnimalia, Chordata, Vertebrata, Osteichthyes, Actinopterygii, Neopterygii, Ostariophysi, Characiformes, Characidae
4th rowAnimalia, Chordata, Vertebrata, Osteichthyes, Actinopterygii, Neopterygii, Acanthopterygii, Perciformes, Labroidei, Cichlidae
5th rowAnimalia, Chordata, Vertebrata, Osteichthyes, Actinopterygii, Neopterygii, Paracanthopterygii, Lophiiformes, Ogcocephalioidei, Ogcocephalidae
ValueCountFrequency (%)
animalia 7
10.1%
chordata 7
10.1%
vertebrata 7
10.1%
osteichthyes 7
10.1%
actinopterygii 7
10.1%
neopterygii 7
10.1%
acanthopterygii 4
 
5.8%
perciformes 3
 
4.3%
labroidei 3
 
4.3%
ostariophysi 2
 
2.9%
Other values (15) 15
21.7%
2025-01-02T17:09:17.153460image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 101
 
11.4%
e 82
 
9.2%
a 73
 
8.2%
t 73
 
8.2%
r 66
 
7.4%
, 62
 
7.0%
62
 
7.0%
o 45
 
5.1%
h 34
 
3.8%
c 33
 
3.7%
Other values (21) 256
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 887
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 101
 
11.4%
e 82
 
9.2%
a 73
 
8.2%
t 73
 
8.2%
r 66
 
7.4%
, 62
 
7.0%
62
 
7.0%
o 45
 
5.1%
h 34
 
3.8%
c 33
 
3.7%
Other values (21) 256
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 887
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 101
 
11.4%
e 82
 
9.2%
a 73
 
8.2%
t 73
 
8.2%
r 66
 
7.4%
, 62
 
7.0%
62
 
7.0%
o 45
 
5.1%
h 34
 
3.8%
c 33
 
3.7%
Other values (21) 256
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 887
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 101
 
11.4%
e 82
 
9.2%
a 73
 
8.2%
t 73
 
8.2%
r 66
 
7.4%
, 62
 
7.0%
62
 
7.0%
o 45
 
5.1%
h 34
 
3.8%
c 33
 
3.7%
Other values (21) 256
28.9%

earliestEraOrLowestErathem
Text

Constant  Missing 

Distinct1
Distinct (%)14.3%
Missing455205
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:17.218420image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters56
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnimalia
2nd rowAnimalia
3rd rowAnimalia
4th rowAnimalia
5th rowAnimalia
ValueCountFrequency (%)
animalia 7
100.0%
2025-01-02T17:09:17.342290image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 14
25.0%
i 14
25.0%
n 7
12.5%
A 7
12.5%
m 7
12.5%
l 7
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 14
25.0%
i 14
25.0%
n 7
12.5%
A 7
12.5%
m 7
12.5%
l 7
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 14
25.0%
i 14
25.0%
n 7
12.5%
A 7
12.5%
m 7
12.5%
l 7
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 14
25.0%
i 14
25.0%
n 7
12.5%
A 7
12.5%
m 7
12.5%
l 7
12.5%

latestEraOrHighestErathem
Text

Constant  Missing 

Distinct1
Distinct (%)14.3%
Missing455205
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:17.399203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters56
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChordata
2nd rowChordata
3rd rowChordata
4th rowChordata
5th rowChordata
ValueCountFrequency (%)
chordata 7
100.0%
2025-01-02T17:09:17.508914image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 14
25.0%
h 7
12.5%
C 7
12.5%
o 7
12.5%
r 7
12.5%
d 7
12.5%
t 7
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 14
25.0%
h 7
12.5%
C 7
12.5%
o 7
12.5%
r 7
12.5%
d 7
12.5%
t 7
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 14
25.0%
h 7
12.5%
C 7
12.5%
o 7
12.5%
r 7
12.5%
d 7
12.5%
t 7
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 14
25.0%
h 7
12.5%
C 7
12.5%
o 7
12.5%
r 7
12.5%
d 7
12.5%
t 7
12.5%

earliestPeriodOrLowestSystem
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB
Distinct5
Distinct (%)71.4%
Missing455205
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:17.567450image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length13
Mean length12.14285714
Min length11

Characters and Unicode

Total characters85
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)57.1%

Sample

1st rowSiluriformes
2nd rowPerciformes
3rd rowCharaciformes
4th rowPerciformes
5th rowLophiiformes
ValueCountFrequency (%)
perciformes 3
42.9%
siluriformes 1
 
14.3%
characiformes 1
 
14.3%
lophiiformes 1
 
14.3%
scorpaeniformes 1
 
14.3%
2025-01-02T17:09:17.699825image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 13
15.3%
e 11
12.9%
o 9
10.6%
i 9
10.6%
s 7
8.2%
f 7
8.2%
m 7
8.2%
c 5
 
5.9%
P 3
 
3.5%
a 3
 
3.5%
Other values (8) 11
12.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 85
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 13
15.3%
e 11
12.9%
o 9
10.6%
i 9
10.6%
s 7
8.2%
f 7
8.2%
m 7
8.2%
c 5
 
5.9%
P 3
 
3.5%
a 3
 
3.5%
Other values (8) 11
12.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 85
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 13
15.3%
e 11
12.9%
o 9
10.6%
i 9
10.6%
s 7
8.2%
f 7
8.2%
m 7
8.2%
c 5
 
5.9%
P 3
 
3.5%
a 3
 
3.5%
Other values (8) 11
12.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 85
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 13
15.3%
e 11
12.9%
o 9
10.6%
i 9
10.6%
s 7
8.2%
f 7
8.2%
m 7
8.2%
c 5
 
5.9%
P 3
 
3.5%
a 3
 
3.5%
Other values (8) 11
12.9%

earliestEpochOrLowestSeries
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB
Distinct7
Distinct (%)100.0%
Missing455205
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:17.769636image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length11
Mean length10.71428571
Min length8

Characters and Unicode

Total characters75
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st rowIctaluridae
2nd rowLabridae
3rd rowBryconidae
4th rowCichlidae
5th rowOgcocephalidae
ValueCountFrequency (%)
ictaluridae 1
14.3%
labridae 1
14.3%
bryconidae 1
14.3%
cichlidae 1
14.3%
ogcocephalidae 1
14.3%
scaridae 1
14.3%
hemitripteridae 1
14.3%
2025-01-02T17:09:17.906501image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 11
14.7%
i 10
13.3%
e 10
13.3%
d 7
9.3%
r 6
 
8.0%
c 6
 
8.0%
l 3
 
4.0%
t 3
 
4.0%
p 2
 
2.7%
h 2
 
2.7%
Other values (14) 15
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11
14.7%
i 10
13.3%
e 10
13.3%
d 7
9.3%
r 6
 
8.0%
c 6
 
8.0%
l 3
 
4.0%
t 3
 
4.0%
p 2
 
2.7%
h 2
 
2.7%
Other values (14) 15
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11
14.7%
i 10
13.3%
e 10
13.3%
d 7
9.3%
r 6
 
8.0%
c 6
 
8.0%
l 3
 
4.0%
t 3
 
4.0%
p 2
 
2.7%
h 2
 
2.7%
Other values (14) 15
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11
14.7%
i 10
13.3%
e 10
13.3%
d 7
9.3%
r 6
 
8.0%
c 6
 
8.0%
l 3
 
4.0%
t 3
 
4.0%
p 2
 
2.7%
h 2
 
2.7%
Other values (14) 15
20.0%

earliestAgeOrLowestStage
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

latestAgeOrHighestStage
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

lowestBiostratigraphicZone
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB
Distinct7
Distinct (%)100.0%
Missing455205
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:17.976831image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length14
Median length10
Mean length8.714285714
Min length6

Characters and Unicode

Total characters61
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st rowNoturus
2nd rowThalassoma
3rd rowBrycon
4th rowPseudotropheus
5th rowHalieutaea
ValueCountFrequency (%)
noturus 1
14.3%
thalassoma 1
14.3%
brycon 1
14.3%
pseudotropheus 1
14.3%
halieutaea 1
14.3%
scarus 1
14.3%
blepsias 1
14.3%
2025-01-02T17:09:18.109462image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 8
13.1%
a 8
13.1%
u 6
 
9.8%
o 5
 
8.2%
e 5
 
8.2%
r 4
 
6.6%
l 3
 
4.9%
t 3
 
4.9%
c 2
 
3.3%
h 2
 
3.3%
Other values (12) 15
24.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 8
13.1%
a 8
13.1%
u 6
 
9.8%
o 5
 
8.2%
e 5
 
8.2%
r 4
 
6.6%
l 3
 
4.9%
t 3
 
4.9%
c 2
 
3.3%
h 2
 
3.3%
Other values (12) 15
24.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 8
13.1%
a 8
13.1%
u 6
 
9.8%
o 5
 
8.2%
e 5
 
8.2%
r 4
 
6.6%
l 3
 
4.9%
t 3
 
4.9%
c 2
 
3.3%
h 2
 
3.3%
Other values (12) 15
24.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 8
13.1%
a 8
13.1%
u 6
 
9.8%
o 5
 
8.2%
e 5
 
8.2%
r 4
 
6.6%
l 3
 
4.9%
t 3
 
4.9%
c 2
 
3.3%
h 2
 
3.3%
Other values (12) 15
24.6%
Distinct7
Distinct (%)100.0%
Missing455205
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:18.180094image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length14
Median length10
Mean length8.714285714
Min length6

Characters and Unicode

Total characters61
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st rowNoturus
2nd rowThalassoma
3rd rowBrycon
4th rowPseudotropheus
5th rowHalieutaea
ValueCountFrequency (%)
noturus 1
14.3%
thalassoma 1
14.3%
brycon 1
14.3%
pseudotropheus 1
14.3%
halieutaea 1
14.3%
scarus 1
14.3%
blepsias 1
14.3%
2025-01-02T17:09:18.318310image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 8
13.1%
a 8
13.1%
u 6
 
9.8%
o 5
 
8.2%
e 5
 
8.2%
r 4
 
6.6%
l 3
 
4.9%
t 3
 
4.9%
c 2
 
3.3%
h 2
 
3.3%
Other values (12) 15
24.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 8
13.1%
a 8
13.1%
u 6
 
9.8%
o 5
 
8.2%
e 5
 
8.2%
r 4
 
6.6%
l 3
 
4.9%
t 3
 
4.9%
c 2
 
3.3%
h 2
 
3.3%
Other values (12) 15
24.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 8
13.1%
a 8
13.1%
u 6
 
9.8%
o 5
 
8.2%
e 5
 
8.2%
r 4
 
6.6%
l 3
 
4.9%
t 3
 
4.9%
c 2
 
3.3%
h 2
 
3.3%
Other values (12) 15
24.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 8
13.1%
a 8
13.1%
u 6
 
9.8%
o 5
 
8.2%
e 5
 
8.2%
r 4
 
6.6%
l 3
 
4.9%
t 3
 
4.9%
c 2
 
3.3%
h 2
 
3.3%
Other values (12) 15
24.6%

group
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

formation
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

member
Text

Missing 

Distinct7
Distinct (%)100.0%
Missing455205
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:18.387237image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.571428571
Min length6

Characters and Unicode

Total characters60
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st rownocturnus
2nd rowlunare
3rd rowfalcatus
4th rowelongatus
5th rowbrevicauda
ValueCountFrequency (%)
nocturnus 1
14.3%
lunare 1
14.3%
falcatus 1
14.3%
elongatus 1
14.3%
brevicauda 1
14.3%
globiceps 1
14.3%
cirrhosus 1
14.3%
2025-01-02T17:09:18.522507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
u 7
11.7%
s 6
10.0%
a 6
10.0%
c 5
8.3%
r 5
8.3%
n 4
 
6.7%
o 4
 
6.7%
l 4
 
6.7%
e 4
 
6.7%
t 3
 
5.0%
Other values (8) 12
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 7
11.7%
s 6
10.0%
a 6
10.0%
c 5
8.3%
r 5
8.3%
n 4
 
6.7%
o 4
 
6.7%
l 4
 
6.7%
e 4
 
6.7%
t 3
 
5.0%
Other values (8) 12
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 7
11.7%
s 6
10.0%
a 6
10.0%
c 5
8.3%
r 5
8.3%
n 4
 
6.7%
o 4
 
6.7%
l 4
 
6.7%
e 4
 
6.7%
t 3
 
5.0%
Other values (8) 12
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 7
11.7%
s 6
10.0%
a 6
10.0%
c 5
8.3%
r 5
8.3%
n 4
 
6.7%
o 4
 
6.7%
l 4
 
6.7%
e 4
 
6.7%
t 3
 
5.0%
Other values (8) 12
20.0%

bed
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

identificationID
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

verbatimIdentification
Text

Constant  Missing 

Distinct1
Distinct (%)14.3%
Missing455205
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:18.580645image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters49
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSPECIES
2nd rowSPECIES
3rd rowSPECIES
4th rowSPECIES
5th rowSPECIES
ValueCountFrequency (%)
species 7
100.0%
2025-01-02T17:09:18.692359image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 14
28.6%
E 14
28.6%
P 7
14.3%
C 7
14.3%
I 7
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 14
28.6%
E 14
28.6%
P 7
14.3%
C 7
14.3%
I 7
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 14
28.6%
E 14
28.6%
P 7
14.3%
C 7
14.3%
I 7
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 14
28.6%
E 14
28.6%
P 7
14.3%
C 7
14.3%
I 7
14.3%
Distinct5
Distinct (%)0.3%
Missing453516
Missing (%)99.6%
Memory size3.5 MiB
2025-01-02T17:09:18.751098image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length3
Mean length5.780660377
Min length3

Characters and Unicode

Total characters9804
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcf.
2nd rowuncertain
3rd rowuncertain
4th rowuncertain
5th rownear
ValueCountFrequency (%)
cf 895
52.8%
uncertain 783
46.2%
aff 14
 
0.8%
near 4
 
0.2%
2025-01-02T17:09:18.869277image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 1678
17.1%
n 1570
16.0%
f 923
9.4%
. 909
9.3%
a 801
8.2%
r 787
8.0%
e 787
8.0%
i 783
8.0%
t 783
8.0%
u 652
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9804
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 1678
17.1%
n 1570
16.0%
f 923
9.4%
. 909
9.3%
a 801
8.2%
r 787
8.0%
e 787
8.0%
i 783
8.0%
t 783
8.0%
u 652
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9804
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 1678
17.1%
n 1570
16.0%
f 923
9.4%
. 909
9.3%
a 801
8.2%
r 787
8.0%
e 787
8.0%
i 783
8.0%
t 783
8.0%
u 652
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9804
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 1678
17.1%
n 1570
16.0%
f 923
9.4%
. 909
9.3%
a 801
8.2%
r 787
8.0%
e 787
8.0%
i 783
8.0%
t 783
8.0%
u 652
 
6.7%

typeStatus
Text

Missing 

Distinct9
Distinct (%)< 0.1%
Missing436448
Missing (%)95.9%
Memory size3.5 MiB
2025-01-02T17:09:18.930750image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length8
Mean length7.670219569
Min length4

Characters and Unicode

Total characters143924
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPARATYPE
2nd rowHOLOTYPE
3rd rowPARATYPE
4th rowPARATYPE
5th rowCOTYPE
ValueCountFrequency (%)
paratype 12437
66.3%
holotype 3339
 
17.8%
type 1470
 
7.8%
syntype 819
 
4.4%
cotype 296
 
1.6%
paralectotype 207
 
1.1%
lectotype 127
 
0.7%
neotype 59
 
0.3%
allotype 10
 
0.1%
2025-01-02T17:09:19.150267image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 31408
21.8%
A 25298
17.6%
Y 19583
13.6%
E 19157
13.3%
T 19098
13.3%
R 12644
8.8%
O 7377
 
5.1%
L 3693
 
2.6%
H 3339
 
2.3%
N 878
 
0.6%
Other values (2) 1449
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 143924
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 31408
21.8%
A 25298
17.6%
Y 19583
13.6%
E 19157
13.3%
T 19098
13.3%
R 12644
8.8%
O 7377
 
5.1%
L 3693
 
2.6%
H 3339
 
2.3%
N 878
 
0.6%
Other values (2) 1449
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 143924
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 31408
21.8%
A 25298
17.6%
Y 19583
13.6%
E 19157
13.3%
T 19098
13.3%
R 12644
8.8%
O 7377
 
5.1%
L 3693
 
2.6%
H 3339
 
2.3%
N 878
 
0.6%
Other values (2) 1449
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 143924
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 31408
21.8%
A 25298
17.6%
Y 19583
13.6%
E 19157
13.3%
T 19098
13.3%
R 12644
8.8%
O 7377
 
5.1%
L 3693
 
2.6%
H 3339
 
2.3%
N 878
 
0.6%
Other values (2) 1449
 
1.0%

identifiedBy
Text

Missing 

Distinct572
Distinct (%)1.7%
Missing421073
Missing (%)92.5%
Memory size3.5 MiB
2025-01-02T17:09:19.289872image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length147
Median length137
Mean length21.13904918
Min length5

Characters and Unicode

Total characters721666
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique143 ?
Unique (%)0.4%

Sample

1st rowPezold, Frank; Larson, Helen K.
2nd rowWilliams, Jeffrey T.
3rd rowWilliams, Jeffrey T.
4th rowEschmeyer, William N.
5th rowKarnella, Susan J.
ValueCountFrequency (%)
williams 6495
 
5.8%
jeffrey 6367
 
5.7%
t 6366
 
5.7%
e 4376
 
3.9%
david 4213
 
3.8%
g 4044
 
3.6%
smith 3785
 
3.4%
c 2656
 
2.4%
diane 2526
 
2.3%
pitassy 2526
 
2.3%
Other values (967) 68435
61.2%
2025-01-02T17:09:19.512660image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
77650
 
10.8%
a 55232
 
7.7%
i 54043
 
7.5%
e 50214
 
7.0%
, 37806
 
5.2%
r 34027
 
4.7%
l 32102
 
4.4%
n 30823
 
4.3%
. 26916
 
3.7%
t 26854
 
3.7%
Other values (59) 295999
41.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 721666
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
77650
 
10.8%
a 55232
 
7.7%
i 54043
 
7.5%
e 50214
 
7.0%
, 37806
 
5.2%
r 34027
 
4.7%
l 32102
 
4.4%
n 30823
 
4.3%
. 26916
 
3.7%
t 26854
 
3.7%
Other values (59) 295999
41.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 721666
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
77650
 
10.8%
a 55232
 
7.7%
i 54043
 
7.5%
e 50214
 
7.0%
, 37806
 
5.2%
r 34027
 
4.7%
l 32102
 
4.4%
n 30823
 
4.3%
. 26916
 
3.7%
t 26854
 
3.7%
Other values (59) 295999
41.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 721666
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
77650
 
10.8%
a 55232
 
7.7%
i 54043
 
7.5%
e 50214
 
7.0%
, 37806
 
5.2%
r 34027
 
4.7%
l 32102
 
4.4%
n 30823
 
4.3%
. 26916
 
3.7%
t 26854
 
3.7%
Other values (59) 295999
41.0%

identifiedByID
Text

Constant  Missing 

Distinct1
Distinct (%)14.3%
Missing455205
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:19.571197image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters56
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowACCEPTED
2nd rowACCEPTED
3rd rowACCEPTED
4th rowACCEPTED
5th rowACCEPTED
ValueCountFrequency (%)
accepted 7
100.0%
2025-01-02T17:09:19.678386image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 14
25.0%
E 14
25.0%
A 7
12.5%
P 7
12.5%
T 7
12.5%
D 7
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 14
25.0%
E 14
25.0%
A 7
12.5%
P 7
12.5%
T 7
12.5%
D 7
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 14
25.0%
E 14
25.0%
A 7
12.5%
P 7
12.5%
T 7
12.5%
D 7
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 14
25.0%
E 14
25.0%
A 7
12.5%
P 7
12.5%
T 7
12.5%
D 7
12.5%

dateIdentified
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

identificationReferences
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

identificationVerificationStatus
Text

Constant  Missing 

Distinct1
Distinct (%)14.3%
Missing455205
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:19.749285image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters252
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row821cc27a-e3bb-4bc5-ac34-89ada245069d
2nd row821cc27a-e3bb-4bc5-ac34-89ada245069d
3rd row821cc27a-e3bb-4bc5-ac34-89ada245069d
4th row821cc27a-e3bb-4bc5-ac34-89ada245069d
5th row821cc27a-e3bb-4bc5-ac34-89ada245069d
ValueCountFrequency (%)
821cc27a-e3bb-4bc5-ac34-89ada245069d 7
100.0%
2025-01-02T17:09:19.876891image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 28
11.1%
a 28
11.1%
- 28
11.1%
2 21
8.3%
4 21
8.3%
b 21
8.3%
8 14
 
5.6%
3 14
 
5.6%
9 14
 
5.6%
d 14
 
5.6%
Other values (6) 49
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 252
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 28
11.1%
a 28
11.1%
- 28
11.1%
2 21
8.3%
4 21
8.3%
b 21
8.3%
8 14
 
5.6%
3 14
 
5.6%
9 14
 
5.6%
d 14
 
5.6%
Other values (6) 49
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 252
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 28
11.1%
a 28
11.1%
- 28
11.1%
2 21
8.3%
4 21
8.3%
b 21
8.3%
8 14
 
5.6%
3 14
 
5.6%
9 14
 
5.6%
d 14
 
5.6%
Other values (6) 49
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 252
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 28
11.1%
a 28
11.1%
- 28
11.1%
2 21
8.3%
4 21
8.3%
b 21
8.3%
8 14
 
5.6%
3 14
 
5.6%
9 14
 
5.6%
d 14
 
5.6%
Other values (6) 49
19.4%

identificationRemarks
Text

Constant  Missing 

Distinct1
Distinct (%)14.3%
Missing455205
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:19.912004image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters14
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
us 7
100.0%
2025-01-02T17:09:20.002589image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 7
50.0%
S 7
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 7
50.0%
S 7
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 7
50.0%
S 7
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 7
50.0%
S 7
50.0%

taxonID
Text

Missing 

Distinct7
Distinct (%)100.0%
Missing455205
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:20.072941image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters168
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st row2024-12-02T13:57:35.184Z
2nd row2024-12-02T13:58:31.286Z
3rd row2024-12-02T13:56:38.525Z
4th row2024-12-02T13:59:43.862Z
5th row2024-12-02T13:56:47.781Z
ValueCountFrequency (%)
2024-12-02t13:57:35.184z 1
14.3%
2024-12-02t13:58:31.286z 1
14.3%
2024-12-02t13:56:38.525z 1
14.3%
2024-12-02t13:59:43.862z 1
14.3%
2024-12-02t13:56:47.781z 1
14.3%
2024-12-02t13:59:40.809z 1
14.3%
2024-12-02t13:58:46.380z 1
14.3%
2025-01-02T17:09:20.205817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 31
18.5%
0 17
10.1%
1 17
10.1%
- 14
8.3%
: 14
8.3%
3 12
 
7.1%
4 12
 
7.1%
5 10
 
6.0%
8 9
 
5.4%
T 7
 
4.2%
Other values (5) 25
14.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 31
18.5%
0 17
10.1%
1 17
10.1%
- 14
8.3%
: 14
8.3%
3 12
 
7.1%
4 12
 
7.1%
5 10
 
6.0%
8 9
 
5.4%
T 7
 
4.2%
Other values (5) 25
14.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 31
18.5%
0 17
10.1%
1 17
10.1%
- 14
8.3%
: 14
8.3%
3 12
 
7.1%
4 12
 
7.1%
5 10
 
6.0%
8 9
 
5.4%
T 7
 
4.2%
Other values (5) 25
14.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 31
18.5%
0 17
10.1%
1 17
10.1%
- 14
8.3%
: 14
8.3%
3 12
 
7.1%
4 12
 
7.1%
5 10
 
6.0%
8 9
 
5.4%
T 7
 
4.2%
Other values (5) 25
14.9%

scientificNameID
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

acceptedNameUsageID
Real number (ℝ)

Distinct22054
Distinct (%)4.8%
Missing211
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2959189.521
Minimum44
Maximum12381359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:20.273095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile8612
Q12365449
median2391041
Q32411792
95-th percentile5853528
Maximum12381359
Range12381315
Interquartile range (IQR)46343

Descriptive statistics

Standard deviation1683886.377
Coefficient of variation (CV)0.5690363408
Kurtosis4.277211668
Mean2959189.521
Median Absolute Deviation (MAD)22393
Skewness1.765985661
Sum1.346434191 × 1012
Variance2.83547333 × 1012
MonotonicityNot monotonic
2025-01-02T17:09:20.348405image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4274 1630
 
0.4%
2360481 1121
 
0.2%
2359014 1113
 
0.2%
2359823 1006
 
0.2%
2376138 1001
 
0.2%
2366967 904
 
0.2%
2367736 893
 
0.2%
2394503 857
 
0.2%
2361357 853
 
0.2%
2365441 760
 
0.2%
Other values (22044) 444863
97.7%
ValueCountFrequency (%)
44 198
< 0.1%
121 2
 
< 0.1%
495 140
< 0.1%
537 13
 
< 0.1%
547 5
 
< 0.1%
ValueCountFrequency (%)
12381359 13
< 0.1%
12322426 1
 
< 0.1%
12306692 3
 
< 0.1%
12290919 1
 
< 0.1%
12283882 8
< 0.1%

parentNameUsageID
Real number (ℝ)

Missing 

Distinct3
Distinct (%)100.0%
Missing455209
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean3.666666667
Minimum0.5
Maximum8.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:20.403684image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.65
Q11.25
median2
Q35.25
95-th percentile7.85
Maximum8.5
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.252450274
Coefficient of variation (CV)1.159759166
Kurtosisnan
Mean3.666666667
Median Absolute Deviation (MAD)1.5
Skewness1.492769076
Sum11
Variance18.08333333
MonotonicityNot monotonic
2025-01-02T17:09:20.455498image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
0.5 1
 
< 0.1%
8.5 1
 
< 0.1%
2 1
 
< 0.1%
(Missing) 455209
> 99.9%
ValueCountFrequency (%)
0.5 1
< 0.1%
2 1
< 0.1%
8.5 1
< 0.1%
ValueCountFrequency (%)
8.5 1
< 0.1%
2 1
< 0.1%
0.5 1
< 0.1%

originalNameUsageID
Real number (ℝ)

Missing 

Distinct2
Distinct (%)66.7%
Missing455209
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean1
Minimum0.5
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:20.502407image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q10.5
median0.5
Q31.25
95-th percentile1.85
Maximum2
Range1.5
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.8660254038
Coefficient of variation (CV)0.8660254038
Kurtosisnan
Mean1
Median Absolute Deviation (MAD)0
Skewness1.732050808
Sum3
Variance0.75
MonotonicityIncreasing
2025-01-02T17:09:20.548059image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0.5 2
 
< 0.1%
2 1
 
< 0.1%
(Missing) 455209
> 99.9%
ValueCountFrequency (%)
0.5 2
< 0.1%
2 1
< 0.1%
ValueCountFrequency (%)
2 1
< 0.1%
0.5 2
< 0.1%

nameAccordingToID
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

namePublishedInID
Text

Missing 

Distinct3
Distinct (%)42.9%
Missing455205
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:20.626496image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length153
Median length48
Mean length86.42857143
Min length48

Characters and Unicode

Total characters605
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)14.3%

Sample

1st rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT
2nd rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT;GEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_DERIVED_FROM_COORDINATES;CONTINENT_INVALID
3rd rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT
4th rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT
5th rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT;GEODETIC_DATUM_ASSUMED_WGS84;GEODETIC_DATUM_INVALID;CONTINENT_DERIVED_FROM_COORDINATES;CONTINENT_INVALID
ValueCountFrequency (%)
occurrence_status_inferred_from_individual_count 4
57.1%
occurrence_status_inferred_from_individual_count;geodetic_datum_assumed_wgs84;continent_derived_from_coordinates;continent_invalid 2
28.6%
occurrence_status_inferred_from_individual_count;geodetic_datum_assumed_wgs84;geodetic_datum_invalid;continent_derived_from_coordinates;continent_invalid 1
 
14.3%
2025-01-02T17:09:20.753032image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 58
9.6%
E 54
 
8.9%
N 53
 
8.8%
I 52
 
8.6%
D 45
 
7.4%
T 44
 
7.3%
R 44
 
7.3%
C 41
 
6.8%
O 40
 
6.6%
U 35
 
5.8%
Other values (11) 139
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 605
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 58
9.6%
E 54
 
8.9%
N 53
 
8.8%
I 52
 
8.6%
D 45
 
7.4%
T 44
 
7.3%
R 44
 
7.3%
C 41
 
6.8%
O 40
 
6.6%
U 35
 
5.8%
Other values (11) 139
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 605
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 58
9.6%
E 54
 
8.9%
N 53
 
8.8%
I 52
 
8.6%
D 45
 
7.4%
T 44
 
7.3%
R 44
 
7.3%
C 41
 
6.8%
O 40
 
6.6%
U 35
 
5.8%
Other values (11) 139
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 605
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 58
9.6%
E 54
 
8.9%
N 53
 
8.8%
I 52
 
8.6%
D 45
 
7.4%
T 44
 
7.3%
R 44
 
7.3%
C 41
 
6.8%
O 40
 
6.6%
U 35
 
5.8%
Other values (11) 139
23.0%

taxonConceptID
Text

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing455210
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:20.810433image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters20
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStillImage
2nd rowStillImage
ValueCountFrequency (%)
stillimage 2
100.0%
2025-01-02T17:09:20.916050image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 4
20.0%
S 2
10.0%
t 2
10.0%
i 2
10.0%
I 2
10.0%
m 2
10.0%
a 2
10.0%
g 2
10.0%
e 2
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 4
20.0%
S 2
10.0%
t 2
10.0%
i 2
10.0%
I 2
10.0%
m 2
10.0%
a 2
10.0%
g 2
10.0%
e 2
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 4
20.0%
S 2
10.0%
t 2
10.0%
i 2
10.0%
I 2
10.0%
m 2
10.0%
a 2
10.0%
g 2
10.0%
e 2
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 4
20.0%
S 2
10.0%
t 2
10.0%
i 2
10.0%
I 2
10.0%
m 2
10.0%
a 2
10.0%
g 2
10.0%
e 2
10.0%
Distinct28366
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:21.066875image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length111
Median length81
Mean length34.33414102
Min length4

Characters and Unicode

Total characters15629313
Distinct characters91
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8055 ?
Unique (%)1.8%

Sample

1st rowEchidna nebulosa (Ahl, 1789)
2nd rowMugil Linnaeus, 1758
3rd rowCryptocentrus filifer (Valenciennes, 1837)
4th rowRhinichthys cataractae (Valenciennes, 1842)
5th rowCentropomus ensiferus Poey, 1860
ValueCountFrequency (%)
74427
 
4.0%
linnaeus 26768
 
1.4%
bleeker 23949
 
1.3%
1758 20993
 
1.1%
valenciennes 20020
 
1.1%
cuvier 18941
 
1.0%
jordan 16870
 
0.9%
bloch 15687
 
0.8%
lacepède 13855
 
0.7%
1801 13309
 
0.7%
Other values (20362) 1613420
86.8%
2025-01-02T17:09:21.320262image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1403027
 
9.0%
e 1045274
 
6.7%
a 1034703
 
6.6%
i 926974
 
5.9%
s 920415
 
5.9%
n 772411
 
4.9%
r 768150
 
4.9%
o 766823
 
4.9%
u 641083
 
4.1%
l 587298
 
3.8%
Other values (81) 6763155
43.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15629313
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1403027
 
9.0%
e 1045274
 
6.7%
a 1034703
 
6.6%
i 926974
 
5.9%
s 920415
 
5.9%
n 772411
 
4.9%
r 768150
 
4.9%
o 766823
 
4.9%
u 641083
 
4.1%
l 587298
 
3.8%
Other values (81) 6763155
43.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15629313
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1403027
 
9.0%
e 1045274
 
6.7%
a 1034703
 
6.6%
i 926974
 
5.9%
s 920415
 
5.9%
n 772411
 
4.9%
r 768150
 
4.9%
o 766823
 
4.9%
u 641083
 
4.1%
l 587298
 
3.8%
Other values (81) 6763155
43.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15629313
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1403027
 
9.0%
e 1045274
 
6.7%
a 1034703
 
6.6%
i 926974
 
5.9%
s 920415
 
5.9%
n 772411
 
4.9%
r 768150
 
4.9%
o 766823
 
4.9%
u 641083
 
4.1%
l 587298
 
3.8%
Other values (81) 6763155
43.3%

acceptedNameUsage
Boolean

Constant  Missing 

Distinct1
Distinct (%)14.3%
Missing455205
Missing (%)> 99.9%
Memory size3.5 MiB
False
 
7
(Missing)
455205 
ValueCountFrequency (%)
False 7
 
< 0.1%
(Missing) 455205
> 99.9%
2025-01-02T17:09:21.382092image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

parentNameUsage
Real number (ℝ)

Missing 

Distinct7
Distinct (%)100.0%
Missing455205
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean2370838.571
Minimum2335095
Maximum2414948
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:21.423478image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2335095
5-th percentile2336877.3
Q12347255.5
median2373066
Q32389125
95-th percentile2408598.2
Maximum2414948
Range79853
Interquartile range (IQR)41869.5

Descriptive statistics

Standard deviation29240.54018
Coefficient of variation (CV)0.01233341676
Kurtosis-1.180951489
Mean2370838.571
Median Absolute Deviation (MAD)20716
Skewness0.2153112136
Sum16595870
Variance855009190
MonotonicityNot monotonic
2025-01-02T17:09:21.477530image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2341036 1
 
< 0.1%
2384468 1
 
< 0.1%
2353475 1
 
< 0.1%
2373066 1
 
< 0.1%
2414948 1
 
< 0.1%
2393782 1
 
< 0.1%
2335095 1
 
< 0.1%
(Missing) 455205
> 99.9%
ValueCountFrequency (%)
2335095 1
< 0.1%
2341036 1
< 0.1%
2353475 1
< 0.1%
2373066 1
< 0.1%
2384468 1
< 0.1%
ValueCountFrequency (%)
2414948 1
< 0.1%
2393782 1
< 0.1%
2384468 1
< 0.1%
2373066 1
< 0.1%
2353475 1
< 0.1%

originalNameUsage
Real number (ℝ)

Missing 

Distinct7
Distinct (%)100.0%
Missing455205
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean2370838.571
Minimum2335095
Maximum2414948
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:21.532907image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2335095
5-th percentile2336877.3
Q12347255.5
median2373066
Q32389125
95-th percentile2408598.2
Maximum2414948
Range79853
Interquartile range (IQR)41869.5

Descriptive statistics

Standard deviation29240.54018
Coefficient of variation (CV)0.01233341676
Kurtosis-1.180951489
Mean2370838.571
Median Absolute Deviation (MAD)20716
Skewness0.2153112136
Sum16595870
Variance855009190
MonotonicityNot monotonic
2025-01-02T17:09:21.584273image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2341036 1
 
< 0.1%
2384468 1
 
< 0.1%
2353475 1
 
< 0.1%
2373066 1
 
< 0.1%
2414948 1
 
< 0.1%
2393782 1
 
< 0.1%
2335095 1
 
< 0.1%
(Missing) 455205
> 99.9%
ValueCountFrequency (%)
2335095 1
< 0.1%
2341036 1
< 0.1%
2353475 1
< 0.1%
2373066 1
< 0.1%
2384468 1
< 0.1%
ValueCountFrequency (%)
2414948 1
< 0.1%
2393782 1
< 0.1%
2384468 1
< 0.1%
2373066 1
< 0.1%
2353475 1
< 0.1%

nameAccordingTo
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)14.3%
Missing455205
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean1
Minimum1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:21.634190image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean1
Median Absolute Deviation (MAD)0
Skewness0
Sum7
Variance0
MonotonicityIncreasing
2025-01-02T17:09:21.681355image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1 7
 
< 0.1%
(Missing) 455205
> 99.9%
ValueCountFrequency (%)
1 7
< 0.1%
ValueCountFrequency (%)
1 7
< 0.1%

namePublishedIn
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)14.3%
Missing455205
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean44
Minimum44
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:21.727194image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile44
Q144
median44
Q344
95-th percentile44
Maximum44
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean44
Median Absolute Deviation (MAD)0
Skewness0
Sum308
Variance0
MonotonicityIncreasing
2025-01-02T17:09:21.772116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
44 7
 
< 0.1%
(Missing) 455205
> 99.9%
ValueCountFrequency (%)
44 7
< 0.1%
ValueCountFrequency (%)
44 7
< 0.1%

namePublishedInYear
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB
Distinct868
Distinct (%)0.2%
Missing231
Missing (%)0.1%
Memory size3.5 MiB
2025-01-02T17:09:21.892562image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length164
Median length155
Mean length131.5133379
Min length3

Characters and Unicode

Total characters59836070
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)< 0.1%

Sample

1st rowAnimalia, Chordata, Vertebrata, Osteichthyes, Actinopterygii, Neopterygii, Elopomorpha, Anguilliformes, Muraenoidei, Muraenidae, Muraeninae
2nd rowAnimalia, Chordata, Vertebrata, Osteichthyes, Actinopterygii, Neopterygii, Acanthopterygii, Perciformes, Percoidei, Mugilidae
3rd rowAnimalia, Chordata, Vertebrata, Osteichthyes, Actinopterygii, Neopterygii, Acanthopterygii, Perciformes, Gobioidei, Gobiidae, Gobiinae
4th rowAnimalia, Chordata, Vertebrata, Osteichthyes, Actinopterygii, Neopterygii, Ostariophysi, Cypriniformes, Cyprinidae
5th rowAnimalia, Chordata, Vertebrata, Osteichthyes, Actinopterygii, Neopterygii, Acanthopterygii, Perciformes, Percoidei, Centropomidae
ValueCountFrequency (%)
chordata 454965
 
9.9%
animalia 454921
 
9.9%
vertebrata 454410
 
9.8%
osteichthyes 444515
 
9.6%
actinopterygii 444459
 
9.6%
neopterygii 444025
 
9.6%
acanthopterygii 293090
 
6.4%
perciformes 213808
 
4.6%
percoidei 96925
 
2.1%
ostariophysi 67590
 
1.5%
Other values (974) 1246012
27.0%
2025-01-02T17:09:22.203613image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 6690834
 
11.2%
e 5764651
 
9.6%
t 4862198
 
8.1%
a 4453200
 
7.4%
4159739
 
7.0%
, 4159739
 
7.0%
r 4156105
 
6.9%
o 3437162
 
5.7%
h 2157318
 
3.6%
n 2101258
 
3.5%
Other values (48) 17893866
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 59836070
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 6690834
 
11.2%
e 5764651
 
9.6%
t 4862198
 
8.1%
a 4453200
 
7.4%
4159739
 
7.0%
, 4159739
 
7.0%
r 4156105
 
6.9%
o 3437162
 
5.7%
h 2157318
 
3.6%
n 2101258
 
3.5%
Other values (48) 17893866
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 59836070
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 6690834
 
11.2%
e 5764651
 
9.6%
t 4862198
 
8.1%
a 4453200
 
7.4%
4159739
 
7.0%
, 4159739
 
7.0%
r 4156105
 
6.9%
o 3437162
 
5.7%
h 2157318
 
3.6%
n 2101258
 
3.5%
Other values (48) 17893866
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 59836070
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 6690834
 
11.2%
e 5764651
 
9.6%
t 4862198
 
8.1%
a 4453200
 
7.4%
4159739
 
7.0%
, 4159739
 
7.0%
r 4156105
 
6.9%
o 3437162
 
5.7%
h 2157318
 
3.6%
n 2101258
 
3.5%
Other values (48) 17893866
29.9%
Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:22.273343image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length14
Median length8
Mean length8.00267348
Min length4

Characters and Unicode

Total characters3642913
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowAnimalia
2nd rowAnimalia
3rd rowAnimalia
4th rowAnimalia
5th rowAnimalia
ValueCountFrequency (%)
animalia 454998
99.9%
incertae 207
 
< 0.1%
sedis 207
 
< 0.1%
5153 1
 
< 0.1%
8535 1
 
< 0.1%
6880497 1
 
< 0.1%
8522 1
 
< 0.1%
4215 1
 
< 0.1%
4504 1
 
< 0.1%
5097 1
 
< 0.1%
2025-01-02T17:09:22.386781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 910410
25.0%
a 910203
25.0%
n 455205
12.5%
A 454998
12.5%
m 454998
12.5%
l 454998
12.5%
e 621
 
< 0.1%
s 414
 
< 0.1%
c 207
 
< 0.1%
t 207
 
< 0.1%
Other values (13) 652
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3642913
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 910410
25.0%
a 910203
25.0%
n 455205
12.5%
A 454998
12.5%
m 454998
12.5%
l 454998
12.5%
e 621
 
< 0.1%
s 414
 
< 0.1%
c 207
 
< 0.1%
t 207
 
< 0.1%
Other values (13) 652
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3642913
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 910410
25.0%
a 910203
25.0%
n 455205
12.5%
A 454998
12.5%
m 454998
12.5%
l 454998
12.5%
e 621
 
< 0.1%
s 414
 
< 0.1%
c 207
 
< 0.1%
t 207
 
< 0.1%
Other values (13) 652
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3642913
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 910410
25.0%
a 910203
25.0%
n 455205
12.5%
A 454998
12.5%
m 454998
12.5%
l 454998
12.5%
e 621
 
< 0.1%
s 414
 
< 0.1%
c 207
 
< 0.1%
t 207
 
< 0.1%
Other values (13) 652
 
< 0.1%

phylum
Text

Distinct9
Distinct (%)< 0.1%
Missing285
Missing (%)0.1%
Memory size3.5 MiB
2025-01-02T17:09:22.448354image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length8
Mean length8.000015387
Min length7

Characters and Unicode

Total characters3639423
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowChordata
2nd rowChordata
3rd rowChordata
4th rowChordata
5th rowChordata
ValueCountFrequency (%)
chordata 454913
> 99.9%
arthropoda 7
 
< 0.1%
2341007 1
 
< 0.1%
2384450 1
 
< 0.1%
2353451 1
 
< 0.1%
2373062 1
 
< 0.1%
2414937 1
 
< 0.1%
2371535 1
 
< 0.1%
2335094 1
 
< 0.1%
2025-01-02T17:09:22.572097image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 909833
25.0%
r 454927
12.5%
o 454927
12.5%
d 454920
12.5%
h 454920
12.5%
t 454920
12.5%
C 454913
12.5%
3 11
 
< 0.1%
2 8
 
< 0.1%
p 7
 
< 0.1%
Other values (9) 37
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3639423
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 909833
25.0%
r 454927
12.5%
o 454927
12.5%
d 454920
12.5%
h 454920
12.5%
t 454920
12.5%
C 454913
12.5%
3 11
 
< 0.1%
2 8
 
< 0.1%
p 7
 
< 0.1%
Other values (9) 37
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3639423
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 909833
25.0%
r 454927
12.5%
o 454927
12.5%
d 454920
12.5%
h 454920
12.5%
t 454920
12.5%
C 454913
12.5%
3 11
 
< 0.1%
2 8
 
< 0.1%
p 7
 
< 0.1%
Other values (9) 37
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3639423
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 909833
25.0%
r 454927
12.5%
o 454927
12.5%
d 454920
12.5%
h 454920
12.5%
t 454920
12.5%
C 454913
12.5%
3 11
 
< 0.1%
2 8
 
< 0.1%
p 7
 
< 0.1%
Other values (9) 37
 
< 0.1%

class
Text

Missing 

Distinct9
Distinct (%)0.1%
Missing444746
Missing (%)97.7%
Memory size3.5 MiB
2025-01-02T17:09:22.635613image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length14
Median length14
Mean length13.50496847
Min length6

Characters and Unicode

Total characters141343
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowElasmobranchii
2nd rowPetromyzonti
3rd rowPetromyzonti
4th rowElasmobranchii
5th rowElasmobranchii
ValueCountFrequency (%)
elasmobranchii 8825
84.3%
petromyzonti 565
 
5.4%
leptocardii 514
 
4.9%
holocephali 362
 
3.5%
myxini 150
 
1.4%
dipneusti 28
 
0.3%
coelacanthi 14
 
0.1%
arachnida 7
 
0.1%
amphibia 1
 
< 0.1%
2025-01-02T17:09:22.769056image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 19984
14.1%
a 18569
13.1%
o 11207
7.9%
r 9911
 
7.0%
c 9722
 
6.9%
n 9589
 
6.8%
l 9563
 
6.8%
m 9391
 
6.6%
h 9209
 
6.5%
s 8853
 
6.3%
Other values (17) 25345
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 141343
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 19984
14.1%
a 18569
13.1%
o 11207
7.9%
r 9911
 
7.0%
c 9722
 
6.9%
n 9589
 
6.8%
l 9563
 
6.8%
m 9391
 
6.6%
h 9209
 
6.5%
s 8853
 
6.3%
Other values (17) 25345
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 141343
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 19984
14.1%
a 18569
13.1%
o 11207
7.9%
r 9911
 
7.0%
c 9722
 
6.9%
n 9589
 
6.8%
l 9563
 
6.8%
m 9391
 
6.6%
h 9209
 
6.5%
s 8853
 
6.3%
Other values (17) 25345
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 141343
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 19984
14.1%
a 18569
13.1%
o 11207
7.9%
r 9911
 
7.0%
c 9722
 
6.9%
n 9589
 
6.8%
l 9563
 
6.8%
m 9391
 
6.6%
h 9209
 
6.5%
s 8853
 
6.3%
Other values (17) 25345
17.9%

order
Text

Distinct71
Distinct (%)< 0.1%
Missing1001
Missing (%)0.2%
Memory size3.5 MiB
2025-01-02T17:09:22.867488image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length19
Mean length12.46148376
Min length7

Characters and Unicode

Total characters5660143
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowAnguilliformes
2nd rowMugiliformes
3rd rowPerciformes
4th rowCypriniformes
5th rowPerciformes
ValueCountFrequency (%)
perciformes 212582
46.8%
cypriniformes 33752
 
7.4%
scorpaeniformes 17672
 
3.9%
characiformes 17478
 
3.8%
anguilliformes 17113
 
3.8%
siluriformes 14280
 
3.1%
myctophiformes 13708
 
3.0%
pleuronectiformes 12320
 
2.7%
stomiiformes 12085
 
2.7%
tetraodontiformes 10526
 
2.3%
Other values (61) 92695
20.4%
2025-01-02T17:09:23.029643image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 810127
14.3%
e 762624
13.5%
o 587988
10.4%
i 567492
10.0%
m 475972
8.4%
s 464456
8.2%
f 454197
8.0%
c 292652
 
5.2%
P 226159
 
4.0%
n 146439
 
2.6%
Other values (38) 872037
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5660143
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 810127
14.3%
e 762624
13.5%
o 587988
10.4%
i 567492
10.0%
m 475972
8.4%
s 464456
8.2%
f 454197
8.0%
c 292652
 
5.2%
P 226159
 
4.0%
n 146439
 
2.6%
Other values (38) 872037
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5660143
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 810127
14.3%
e 762624
13.5%
o 587988
10.4%
i 567492
10.0%
m 475972
8.4%
s 464456
8.2%
f 454197
8.0%
c 292652
 
5.2%
P 226159
 
4.0%
n 146439
 
2.6%
Other values (38) 872037
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5660143
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 810127
14.3%
e 762624
13.5%
o 587988
10.4%
i 567492
10.0%
m 475972
8.4%
s 464456
8.2%
f 454197
8.0%
c 292652
 
5.2%
P 226159
 
4.0%
n 146439
 
2.6%
Other values (38) 872037
15.4%

superfamily
Text

Missing 

Distinct7
Distinct (%)100.0%
Missing455205
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:23.110102image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length18
Mean length18.28571429
Min length15

Characters and Unicode

Total characters128
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st rowNoturus nocturnus
2nd rowThalassoma lunare
3rd rowBrycon falcatus
4th rowPseudotropheus elongatus
5th rowHalieutaea brevicauda
ValueCountFrequency (%)
noturus 1
 
7.1%
nocturnus 1
 
7.1%
thalassoma 1
 
7.1%
lunare 1
 
7.1%
brycon 1
 
7.1%
falcatus 1
 
7.1%
pseudotropheus 1
 
7.1%
elongatus 1
 
7.1%
halieutaea 1
 
7.1%
brevicauda 1
 
7.1%
Other values (4) 4
28.6%
2025-01-02T17:09:23.239324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 14
10.9%
a 14
10.9%
u 13
 
10.2%
r 9
 
7.0%
o 9
 
7.0%
e 9
 
7.0%
7
 
5.5%
c 7
 
5.5%
l 7
 
5.5%
t 6
 
4.7%
Other values (17) 33
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 128
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 14
10.9%
a 14
10.9%
u 13
 
10.2%
r 9
 
7.0%
o 9
 
7.0%
e 9
 
7.0%
7
 
5.5%
c 7
 
5.5%
l 7
 
5.5%
t 6
 
4.7%
Other values (17) 33
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 128
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 14
10.9%
a 14
10.9%
u 13
 
10.2%
r 9
 
7.0%
o 9
 
7.0%
e 9
 
7.0%
7
 
5.5%
c 7
 
5.5%
l 7
 
5.5%
t 6
 
4.7%
Other values (17) 33
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 128
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 14
10.9%
a 14
10.9%
u 13
 
10.2%
r 9
 
7.0%
o 9
 
7.0%
e 9
 
7.0%
7
 
5.5%
c 7
 
5.5%
l 7
 
5.5%
t 6
 
4.7%
Other values (17) 33
25.8%

family
Text

Distinct561
Distinct (%)0.1%
Missing833
Missing (%)0.2%
Memory size3.5 MiB
2025-01-02T17:09:23.360089image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length40
Median length36
Mean length10.79361062
Min length6

Characters and Unicode

Total characters4904390
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)< 0.1%

Sample

1st rowMuraenidae
2nd rowMugilidae
3rd rowGobiidae
4th rowCyprinidae
5th rowCentropomidae
ValueCountFrequency (%)
cyprinidae 27640
 
6.1%
gobiidae 26017
 
5.7%
pomacentridae 16208
 
3.6%
labridae 14638
 
3.2%
blenniidae 14508
 
3.2%
myctophidae 13553
 
3.0%
apogonidae 12381
 
2.7%
serranidae 11376
 
2.5%
characidae 9124
 
2.0%
stomiidae 7881
 
1.7%
Other values (575) 301078
66.3%
2025-01-02T17:09:23.557178image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 707945
14.4%
e 650924
13.3%
i 646771
13.2%
d 489722
10.0%
o 279193
 
5.7%
r 276574
 
5.6%
n 253779
 
5.2%
t 211871
 
4.3%
c 160423
 
3.3%
h 139416
 
2.8%
Other values (53) 1087772
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4904390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 707945
14.4%
e 650924
13.3%
i 646771
13.2%
d 489722
10.0%
o 279193
 
5.7%
r 276574
 
5.6%
n 253779
 
5.2%
t 211871
 
4.3%
c 160423
 
3.3%
h 139416
 
2.8%
Other values (53) 1087772
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4904390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 707945
14.4%
e 650924
13.3%
i 646771
13.2%
d 489722
10.0%
o 279193
 
5.7%
r 276574
 
5.6%
n 253779
 
5.2%
t 211871
 
4.3%
c 160423
 
3.3%
h 139416
 
2.8%
Other values (53) 1087772
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4904390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 707945
14.4%
e 650924
13.3%
i 646771
13.2%
d 489722
10.0%
o 279193
 
5.7%
r 276574
 
5.6%
n 253779
 
5.2%
t 211871
 
4.3%
c 160423
 
3.3%
h 139416
 
2.8%
Other values (53) 1087772
22.2%

subfamily
Text

Missing 

Distinct7
Distinct (%)100.0%
Missing455205
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:23.643405image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length18
Mean length18.28571429
Min length15

Characters and Unicode

Total characters128
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st rowNoturus nocturnus
2nd rowThalassoma lunare
3rd rowBrycon falcatus
4th rowPseudotropheus elongatus
5th rowHalieutaea brevicauda
ValueCountFrequency (%)
noturus 1
 
7.1%
nocturnus 1
 
7.1%
thalassoma 1
 
7.1%
lunare 1
 
7.1%
brycon 1
 
7.1%
falcatus 1
 
7.1%
pseudotropheus 1
 
7.1%
elongatus 1
 
7.1%
halieutaea 1
 
7.1%
brevicauda 1
 
7.1%
Other values (4) 4
28.6%
2025-01-02T17:09:23.787354image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 14
10.9%
a 14
10.9%
u 13
 
10.2%
r 9
 
7.0%
o 9
 
7.0%
e 9
 
7.0%
7
 
5.5%
c 7
 
5.5%
l 7
 
5.5%
t 6
 
4.7%
Other values (17) 33
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 128
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 14
10.9%
a 14
10.9%
u 13
 
10.2%
r 9
 
7.0%
o 9
 
7.0%
e 9
 
7.0%
7
 
5.5%
c 7
 
5.5%
l 7
 
5.5%
t 6
 
4.7%
Other values (17) 33
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 128
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 14
10.9%
a 14
10.9%
u 13
 
10.2%
r 9
 
7.0%
o 9
 
7.0%
e 9
 
7.0%
7
 
5.5%
c 7
 
5.5%
l 7
 
5.5%
t 6
 
4.7%
Other values (17) 33
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 128
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 14
10.9%
a 14
10.9%
u 13
 
10.2%
r 9
 
7.0%
o 9
 
7.0%
e 9
 
7.0%
7
 
5.5%
c 7
 
5.5%
l 7
 
5.5%
t 6
 
4.7%
Other values (17) 33
25.8%

tribe
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

subtribe
Text

Constant  Missing 

Distinct1
Distinct (%)14.3%
Missing455205
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:23.829417image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEML
2nd rowEML
3rd rowEML
4th rowEML
5th rowEML
ValueCountFrequency (%)
eml 7
100.0%
2025-01-02T17:09:23.924552image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 7
33.3%
M 7
33.3%
L 7
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 7
33.3%
M 7
33.3%
L 7
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 7
33.3%
M 7
33.3%
L 7
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 7
33.3%
M 7
33.3%
L 7
33.3%

genus
Text

Missing 

Distinct4427
Distinct (%)1.0%
Missing23586
Missing (%)5.2%
Memory size3.5 MiB
2025-01-02T17:09:24.051760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length19
Mean length9.89925074
Min length3

Characters and Unicode

Total characters4272774
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique411 ?
Unique (%)0.1%

Sample

1st rowEchidna
2nd rowMugil
3rd rowMyersina
4th rowRhinichthys
5th rowCentropomus
ValueCountFrequency (%)
etheostoma 5026
 
1.2%
gymnothorax 4350
 
1.0%
lepomis 4347
 
1.0%
notropis 4334
 
1.0%
chaetodon 4239
 
1.0%
lutjanus 3825
 
0.9%
halichoeres 3118
 
0.7%
chromis 3031
 
0.7%
acanthurus 2923
 
0.7%
pomacentrus 2919
 
0.7%
Other values (4417) 393514
91.2%
2025-01-02T17:09:24.252399image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 399528
 
9.4%
s 397208
 
9.3%
a 333959
 
7.8%
i 300262
 
7.0%
e 279207
 
6.5%
r 261041
 
6.1%
u 247831
 
5.8%
t 244352
 
5.7%
n 224564
 
5.3%
h 210029
 
4.9%
Other values (55) 1374793
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4272774
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 399528
 
9.4%
s 397208
 
9.3%
a 333959
 
7.8%
i 300262
 
7.0%
e 279207
 
6.5%
r 261041
 
6.1%
u 247831
 
5.8%
t 244352
 
5.7%
n 224564
 
5.3%
h 210029
 
4.9%
Other values (55) 1374793
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4272774
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 399528
 
9.4%
s 397208
 
9.3%
a 333959
 
7.8%
i 300262
 
7.0%
e 279207
 
6.5%
r 261041
 
6.1%
u 247831
 
5.8%
t 244352
 
5.7%
n 224564
 
5.3%
h 210029
 
4.9%
Other values (55) 1374793
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4272774
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 399528
 
9.4%
s 397208
 
9.3%
a 333959
 
7.8%
i 300262
 
7.0%
e 279207
 
6.5%
r 261041
 
6.1%
u 247831
 
5.8%
t 244352
 
5.7%
n 224564
 
5.3%
h 210029
 
4.9%
Other values (55) 1374793
32.2%

genericName
Text

Missing 

Distinct5329
Distinct (%)1.2%
Missing23579
Missing (%)5.2%
Memory size3.5 MiB
2025-01-02T17:09:24.386726image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length19
Mean length9.850122674
Min length2

Characters and Unicode

Total characters4251638
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique744 ?
Unique (%)0.2%

Sample

1st rowEchidna
2nd rowMugil
3rd rowCryptocentrus
4th rowRhinichthys
5th rowCentropomus
ValueCountFrequency (%)
notropis 7148
 
1.7%
etheostoma 4849
 
1.1%
gymnothorax 4324
 
1.0%
lepomis 4276
 
1.0%
chaetodon 4249
 
1.0%
lutjanus 3807
 
0.9%
halichoeres 3126
 
0.7%
chromis 3122
 
0.7%
pomacentrus 2956
 
0.7%
acanthurus 2892
 
0.7%
Other values (5319) 390884
90.6%
2025-01-02T17:09:24.588146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 401047
 
9.4%
s 398676
 
9.4%
a 333316
 
7.8%
i 299389
 
7.0%
e 276042
 
6.5%
r 259555
 
6.1%
t 246425
 
5.8%
u 244647
 
5.8%
n 220237
 
5.2%
h 207615
 
4.9%
Other values (52) 1364689
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4251638
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 401047
 
9.4%
s 398676
 
9.4%
a 333316
 
7.8%
i 299389
 
7.0%
e 276042
 
6.5%
r 259555
 
6.1%
t 246425
 
5.8%
u 244647
 
5.8%
n 220237
 
5.2%
h 207615
 
4.9%
Other values (52) 1364689
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4251638
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 401047
 
9.4%
s 398676
 
9.4%
a 333316
 
7.8%
i 299389
 
7.0%
e 276042
 
6.5%
r 259555
 
6.1%
t 246425
 
5.8%
u 244647
 
5.8%
n 220237
 
5.2%
h 207615
 
4.9%
Other values (52) 1364689
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4251638
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 401047
 
9.4%
s 398676
 
9.4%
a 333316
 
7.8%
i 299389
 
7.0%
e 276042
 
6.5%
r 259555
 
6.1%
t 246425
 
5.8%
u 244647
 
5.8%
n 220237
 
5.2%
h 207615
 
4.9%
Other values (52) 1364689
32.1%

subgenus
Boolean

Missing 

Distinct2
Distinct (%)33.3%
Missing455206
Missing (%)> 99.9%
Memory size3.5 MiB
True
 
5
False
 
1
(Missing)
455206 
ValueCountFrequency (%)
True 5
 
< 0.1%
False 1
 
< 0.1%
(Missing) 455206
> 99.9%
2025-01-02T17:09:24.648901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

infragenericEpithet
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

specificEpithet
Text

Missing 

Distinct12528
Distinct (%)3.3%
Missing70259
Missing (%)15.4%
Memory size3.5 MiB
2025-01-02T17:09:24.799657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length17
Mean length8.890235951
Min length2

Characters and Unicode

Total characters3422323
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2693 ?
Unique (%)0.7%

Sample

1st rownebulosa
2nd rowfilifer
3rd rowcataractae
4th rowensiferus
5th rowinferomaculata
ValueCountFrequency (%)
maculatus 1803
 
0.5%
fasciatus 1624
 
0.4%
lineatus 1573
 
0.4%
punctatus 1558
 
0.4%
affinis 1520
 
0.4%
ocellatus 1448
 
0.4%
nigricans 1438
 
0.4%
cornutus 1264
 
0.3%
notatus 1167
 
0.3%
niger 1160
 
0.3%
Other values (12518) 370398
96.2%
2025-01-02T17:09:25.041200image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 384962
11.2%
s 357791
10.5%
i 357450
10.4%
u 276235
 
8.1%
e 241281
 
7.1%
r 227543
 
6.6%
t 215016
 
6.3%
n 207432
 
6.1%
o 194229
 
5.7%
l 192527
 
5.6%
Other values (19) 767857
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3422323
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 384962
11.2%
s 357791
10.5%
i 357450
10.4%
u 276235
 
8.1%
e 241281
 
7.1%
r 227543
 
6.6%
t 215016
 
6.3%
n 207432
 
6.1%
o 194229
 
5.7%
l 192527
 
5.6%
Other values (19) 767857
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3422323
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 384962
11.2%
s 357791
10.5%
i 357450
10.4%
u 276235
 
8.1%
e 241281
 
7.1%
r 227543
 
6.6%
t 215016
 
6.3%
n 207432
 
6.1%
o 194229
 
5.7%
l 192527
 
5.6%
Other values (19) 767857
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3422323
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 384962
11.2%
s 357791
10.5%
i 357450
10.4%
u 276235
 
8.1%
e 241281
 
7.1%
r 227543
 
6.6%
t 215016
 
6.3%
n 207432
 
6.1%
o 194229
 
5.7%
l 192527
 
5.6%
Other values (19) 767857
22.4%

infraspecificEpithet
Text

Missing 

Distinct681
Distinct (%)8.3%
Missing447018
Missing (%)98.2%
Memory size3.5 MiB
2025-01-02T17:09:25.177450image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length16
Mean length8.942762997
Min length3

Characters and Unicode

Total characters73277
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique208 ?
Unique (%)2.5%

Sample

1st rowniloticus
2nd rowramosus
3rd rowvexillare
4th rowvermiculatus
5th rowexilicauda
ValueCountFrequency (%)
leptocephalus 303
 
3.7%
atromaculatus 222
 
2.7%
crocodilus 221
 
2.7%
atratulus 169
 
2.1%
vermiculatus 156
 
1.9%
ferox 145
 
1.8%
commersonnii 138
 
1.7%
interocularis 121
 
1.5%
purpurescens 120
 
1.5%
excisus 114
 
1.4%
Other values (671) 6485
79.1%
2025-01-02T17:09:25.377223image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 8034
11.0%
a 7736
10.6%
i 6963
9.5%
u 6427
8.8%
r 5139
 
7.0%
e 5070
 
6.9%
o 5038
 
6.9%
l 4896
 
6.7%
c 4349
 
5.9%
t 4159
 
5.7%
Other values (17) 15466
21.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 8034
11.0%
a 7736
10.6%
i 6963
9.5%
u 6427
8.8%
r 5139
 
7.0%
e 5070
 
6.9%
o 5038
 
6.9%
l 4896
 
6.7%
c 4349
 
5.9%
t 4159
 
5.7%
Other values (17) 15466
21.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 8034
11.0%
a 7736
10.6%
i 6963
9.5%
u 6427
8.8%
r 5139
 
7.0%
e 5070
 
6.9%
o 5038
 
6.9%
l 4896
 
6.7%
c 4349
 
5.9%
t 4159
 
5.7%
Other values (17) 15466
21.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 8034
11.0%
a 7736
10.6%
i 6963
9.5%
u 6427
8.8%
r 5139
 
7.0%
e 5070
 
6.9%
o 5038
 
6.9%
l 4896
 
6.7%
c 4349
 
5.9%
t 4159
 
5.7%
Other values (17) 15466
21.1%

cultivarEpithet
Text

Missing 

Distinct5
Distinct (%)83.3%
Missing455206
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:25.444290image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length7
Mean length8.166666667
Min length4

Characters and Unicode

Total characters49
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)66.7%

Sample

1st rowNORTH_AMERICA
2nd rowAFRICA
3rd rowLATIN_AMERICA
4th rowAFRICA
5th rowASIA
ValueCountFrequency (%)
africa 2
33.3%
north_america 1
16.7%
latin_america 1
16.7%
asia 1
16.7%
oceania 1
16.7%
2025-01-02T17:09:25.561074image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 13
26.5%
I 7
14.3%
C 5
 
10.2%
R 5
 
10.2%
N 3
 
6.1%
E 3
 
6.1%
F 2
 
4.1%
O 2
 
4.1%
_ 2
 
4.1%
T 2
 
4.1%
Other values (4) 5
 
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 13
26.5%
I 7
14.3%
C 5
 
10.2%
R 5
 
10.2%
N 3
 
6.1%
E 3
 
6.1%
F 2
 
4.1%
O 2
 
4.1%
_ 2
 
4.1%
T 2
 
4.1%
Other values (4) 5
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 13
26.5%
I 7
14.3%
C 5
 
10.2%
R 5
 
10.2%
N 3
 
6.1%
E 3
 
6.1%
F 2
 
4.1%
O 2
 
4.1%
_ 2
 
4.1%
T 2
 
4.1%
Other values (4) 5
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 13
26.5%
I 7
14.3%
C 5
 
10.2%
R 5
 
10.2%
N 3
 
6.1%
E 3
 
6.1%
F 2
 
4.1%
O 2
 
4.1%
_ 2
 
4.1%
T 2
 
4.1%
Other values (4) 5
 
10.2%
Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:25.621287image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length7
Mean length6.796793582
Min length5

Characters and Unicode

Total characters3093982
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSPECIES
2nd rowGENUS
3rd rowSPECIES
4th rowSPECIES
5th rowSPECIES
ValueCountFrequency (%)
species 376767
82.8%
genus 46673
 
10.3%
family 22827
 
5.0%
subspecies 8175
 
1.8%
order 347
 
0.1%
kingdom 204
 
< 0.1%
phylum 198
 
< 0.1%
variety 12
 
< 0.1%
north_america 7
 
< 0.1%
class 2
 
< 0.1%
2025-01-02T17:09:25.745454image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 824736
26.7%
E 816923
26.4%
I 407992
13.2%
P 385140
12.4%
C 384951
12.4%
U 55046
 
1.8%
N 46884
 
1.5%
G 46877
 
1.5%
M 23236
 
0.8%
Y 23037
 
0.7%
Other values (12) 79160
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3093982
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 824736
26.7%
E 816923
26.4%
I 407992
13.2%
P 385140
12.4%
C 384951
12.4%
U 55046
 
1.8%
N 46884
 
1.5%
G 46877
 
1.5%
M 23236
 
0.8%
Y 23037
 
0.7%
Other values (12) 79160
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3093982
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 824736
26.7%
E 816923
26.4%
I 407992
13.2%
P 385140
12.4%
C 384951
12.4%
U 55046
 
1.8%
N 46884
 
1.5%
G 46877
 
1.5%
M 23236
 
0.8%
Y 23037
 
0.7%
Other values (12) 79160
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3093982
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 824736
26.7%
E 816923
26.4%
I 407992
13.2%
P 385140
12.4%
C 384951
12.4%
U 55046
 
1.8%
N 46884
 
1.5%
G 46877
 
1.5%
M 23236
 
0.8%
Y 23037
 
0.7%
Other values (12) 79160
 
2.6%

verbatimTaxonRank
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing455210
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:25.795953image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowSYC
2nd rowPHL
ValueCountFrequency (%)
syc 1
50.0%
phl 1
50.0%
2025-01-02T17:09:25.901594image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 1
16.7%
Y 1
16.7%
C 1
16.7%
P 1
16.7%
H 1
16.7%
L 1
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1
16.7%
Y 1
16.7%
C 1
16.7%
P 1
16.7%
H 1
16.7%
L 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1
16.7%
Y 1
16.7%
C 1
16.7%
P 1
16.7%
H 1
16.7%
L 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1
16.7%
Y 1
16.7%
C 1
16.7%
P 1
16.7%
H 1
16.7%
L 1
16.7%

vernacularName
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing455210
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:25.961007image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length11
Median length10.5
Mean length10.5
Min length10

Characters and Unicode

Total characters21
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowSeychelles
2nd rowPhilippines
ValueCountFrequency (%)
seychelles 1
50.0%
philippines 1
50.0%
2025-01-02T17:09:26.078958image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 4
19.0%
i 3
14.3%
l 3
14.3%
p 2
9.5%
h 2
9.5%
s 2
9.5%
S 1
 
4.8%
y 1
 
4.8%
c 1
 
4.8%
P 1
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4
19.0%
i 3
14.3%
l 3
14.3%
p 2
9.5%
h 2
9.5%
s 2
9.5%
S 1
 
4.8%
y 1
 
4.8%
c 1
 
4.8%
P 1
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4
19.0%
i 3
14.3%
l 3
14.3%
p 2
9.5%
h 2
9.5%
s 2
9.5%
S 1
 
4.8%
y 1
 
4.8%
c 1
 
4.8%
P 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4
19.0%
i 3
14.3%
l 3
14.3%
p 2
9.5%
h 2
9.5%
s 2
9.5%
S 1
 
4.8%
y 1
 
4.8%
c 1
 
4.8%
P 1
 
4.8%

nomenclaturalCode
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing455210
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:26.136796image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters16
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowSYC.20_1
2nd rowPHL.36_1
ValueCountFrequency (%)
syc.20_1 1
50.0%
phl.36_1 1
50.0%
2025-01-02T17:09:26.363179image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2
12.5%
1 2
12.5%
_ 2
12.5%
S 1
 
6.2%
Y 1
 
6.2%
2 1
 
6.2%
C 1
 
6.2%
0 1
 
6.2%
P 1
 
6.2%
H 1
 
6.2%
Other values (3) 3
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 2
12.5%
1 2
12.5%
_ 2
12.5%
S 1
 
6.2%
Y 1
 
6.2%
2 1
 
6.2%
C 1
 
6.2%
0 1
 
6.2%
P 1
 
6.2%
H 1
 
6.2%
Other values (3) 3
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 2
12.5%
1 2
12.5%
_ 2
12.5%
S 1
 
6.2%
Y 1
 
6.2%
2 1
 
6.2%
C 1
 
6.2%
0 1
 
6.2%
P 1
 
6.2%
H 1
 
6.2%
Other values (3) 3
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 2
12.5%
1 2
12.5%
_ 2
12.5%
S 1
 
6.2%
Y 1
 
6.2%
2 1
 
6.2%
C 1
 
6.2%
0 1
 
6.2%
P 1
 
6.2%
H 1
 
6.2%
Other values (3) 3
18.8%
Distinct5
Distinct (%)< 0.1%
Missing209
Missing (%)< 0.1%
Memory size3.5 MiB
2025-01-02T17:09:26.421557image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length8
Mean length7.910209383
Min length6

Characters and Unicode

Total characters3599169
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowACCEPTED
2nd rowACCEPTED
3rd rowSYNONYM
4th rowACCEPTED
5th rowACCEPTED
ValueCountFrequency (%)
accepted 413893
91.0%
synonym 40858
 
9.0%
doubtful 250
 
0.1%
outer 1
 
< 0.1%
islands 1
 
< 0.1%
iloilo 1
 
< 0.1%
2025-01-02T17:09:26.536628image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 827786
23.0%
E 827786
23.0%
D 414143
11.5%
T 414143
11.5%
P 413893
11.5%
A 413893
11.5%
Y 81716
 
2.3%
N 81716
 
2.3%
O 41109
 
1.1%
S 40858
 
1.1%
Other values (18) 42126
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3599169
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 827786
23.0%
E 827786
23.0%
D 414143
11.5%
T 414143
11.5%
P 413893
11.5%
A 413893
11.5%
Y 81716
 
2.3%
N 81716
 
2.3%
O 41109
 
1.1%
S 40858
 
1.1%
Other values (18) 42126
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3599169
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 827786
23.0%
E 827786
23.0%
D 414143
11.5%
T 414143
11.5%
P 413893
11.5%
A 413893
11.5%
Y 81716
 
2.3%
N 81716
 
2.3%
O 41109
 
1.1%
S 40858
 
1.1%
Other values (18) 42126
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3599169
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 827786
23.0%
E 827786
23.0%
D 414143
11.5%
T 414143
11.5%
P 413893
11.5%
A 413893
11.5%
Y 81716
 
2.3%
N 81716
 
2.3%
O 41109
 
1.1%
S 40858
 
1.1%
Other values (18) 42126
 
1.2%

nomenclaturalStatus
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing455211
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:26.598537image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters11
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowPHL.36.21_1
ValueCountFrequency (%)
phl.36.21_1 1
100.0%
2025-01-02T17:09:26.705691image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2
18.2%
1 2
18.2%
P 1
9.1%
L 1
9.1%
H 1
9.1%
3 1
9.1%
6 1
9.1%
2 1
9.1%
_ 1
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 2
18.2%
1 2
18.2%
P 1
9.1%
L 1
9.1%
H 1
9.1%
3 1
9.1%
6 1
9.1%
2 1
9.1%
_ 1
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 2
18.2%
1 2
18.2%
P 1
9.1%
L 1
9.1%
H 1
9.1%
3 1
9.1%
6 1
9.1%
2 1
9.1%
_ 1
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 2
18.2%
1 2
18.2%
P 1
9.1%
L 1
9.1%
H 1
9.1%
3 1
9.1%
6 1
9.1%
2 1
9.1%
_ 1
9.1%

taxonRemarks
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing455211
Missing (%)> 99.9%
Memory size3.5 MiB
2025-01-02T17:09:26.756215image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters11
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowIloilo City
ValueCountFrequency (%)
iloilo 1
50.0%
city 1
50.0%
2025-01-02T17:09:26.859448image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 2
18.2%
o 2
18.2%
i 2
18.2%
I 1
9.1%
1
9.1%
C 1
9.1%
t 1
9.1%
y 1
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2
18.2%
o 2
18.2%
i 2
18.2%
I 1
9.1%
1
9.1%
C 1
9.1%
t 1
9.1%
y 1
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2
18.2%
o 2
18.2%
i 2
18.2%
I 1
9.1%
1
9.1%
C 1
9.1%
t 1
9.1%
y 1
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2
18.2%
o 2
18.2%
i 2
18.2%
I 1
9.1%
1
9.1%
C 1
9.1%
t 1
9.1%
y 1
9.1%
Distinct2
Distinct (%)< 0.1%
Missing6
Missing (%)< 0.1%
Memory size3.5 MiB
2025-01-02T17:09:26.930338image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length36
Mean length35.99995167
Min length14

Characters and Unicode

Total characters16387394
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row821cc27a-e3bb-4bc5-ac34-89ada245069d
2nd row821cc27a-e3bb-4bc5-ac34-89ada245069d
3rd row821cc27a-e3bb-4bc5-ac34-89ada245069d
4th row821cc27a-e3bb-4bc5-ac34-89ada245069d
5th row821cc27a-e3bb-4bc5-ac34-89ada245069d
ValueCountFrequency (%)
821cc27a-e3bb-4bc5-ac34-89ada245069d 455205
> 99.9%
phl.36.21.66_1 1
 
< 0.1%
2025-01-02T17:09:27.056977image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 1820820
11.1%
- 1820820
11.1%
a 1820820
11.1%
2 1365616
8.3%
4 1365615
8.3%
b 1365615
8.3%
3 910411
 
5.6%
8 910410
 
5.6%
5 910410
 
5.6%
9 910410
 
5.6%
Other values (11) 3186447
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16387394
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 1820820
11.1%
- 1820820
11.1%
a 1820820
11.1%
2 1365616
8.3%
4 1365615
8.3%
b 1365615
8.3%
3 910411
 
5.6%
8 910410
 
5.6%
5 910410
 
5.6%
9 910410
 
5.6%
Other values (11) 3186447
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16387394
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 1820820
11.1%
- 1820820
11.1%
a 1820820
11.1%
2 1365616
8.3%
4 1365615
8.3%
b 1365615
8.3%
3 910411
 
5.6%
8 910410
 
5.6%
5 910410
 
5.6%
9 910410
 
5.6%
Other values (11) 3186447
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16387394
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 1820820
11.1%
- 1820820
11.1%
a 1820820
11.1%
2 1365616
8.3%
4 1365615
8.3%
b 1365615
8.3%
3 910411
 
5.6%
8 910410
 
5.6%
5 910410
 
5.6%
9 910410
 
5.6%
Other values (11) 3186447
19.4%
Distinct2
Distinct (%)< 0.1%
Missing6
Missing (%)< 0.1%
Memory size3.5 MiB
2025-01-02T17:09:27.097510image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length2
Mean length2.000015378
Min length2

Characters and Unicode

Total characters910419
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
us 455205
> 99.9%
kahirupan 1
 
< 0.1%
2025-01-02T17:09:27.198663image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 455205
50.0%
S 455205
50.0%
a 2
 
< 0.1%
K 1
 
< 0.1%
h 1
 
< 0.1%
i 1
 
< 0.1%
r 1
 
< 0.1%
u 1
 
< 0.1%
p 1
 
< 0.1%
n 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 910419
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 455205
50.0%
S 455205
50.0%
a 2
 
< 0.1%
K 1
 
< 0.1%
h 1
 
< 0.1%
i 1
 
< 0.1%
r 1
 
< 0.1%
u 1
 
< 0.1%
p 1
 
< 0.1%
n 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 910419
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 455205
50.0%
S 455205
50.0%
a 2
 
< 0.1%
K 1
 
< 0.1%
h 1
 
< 0.1%
i 1
 
< 0.1%
r 1
 
< 0.1%
u 1
 
< 0.1%
p 1
 
< 0.1%
n 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 910419
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 455205
50.0%
S 455205
50.0%
a 2
 
< 0.1%
K 1
 
< 0.1%
h 1
 
< 0.1%
i 1
 
< 0.1%
r 1
 
< 0.1%
u 1
 
< 0.1%
p 1
 
< 0.1%
n 1
 
< 0.1%
Distinct173327
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:27.334160image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length23.99554933
Min length2

Characters and Unicode

Total characters10923062
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50647 ?
Unique (%)11.1%

Sample

1st row2024-12-02T13:56:09.099Z
2nd row2024-12-02T13:56:08.596Z
3rd row2024-12-02T13:59:51.375Z
4th row2024-12-02T13:58:24.571Z
5th row2024-12-02T13:56:08.212Z
ValueCountFrequency (%)
2024-12-02t13:57:01.873z 14
 
< 0.1%
2024-12-02t13:57:53.333z 14
 
< 0.1%
2024-12-02t13:57:28.109z 13
 
< 0.1%
2024-12-02t13:57:41.128z 13
 
< 0.1%
2024-12-02t13:57:52.916z 13
 
< 0.1%
2024-12-02t13:57:30.416z 13
 
< 0.1%
2024-12-02t13:57:03.178z 13
 
< 0.1%
2024-12-02t13:57:04.016z 13
 
< 0.1%
2024-12-02t13:58:01.465z 13
 
< 0.1%
2024-12-02t13:57:28.196z 12
 
< 0.1%
Other values (173317) 455081
> 99.9%
2025-01-02T17:09:27.535579image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2078433
19.0%
0 1154714
10.6%
1 1148157
10.5%
- 910410
8.3%
: 910410
8.3%
4 731701
 
6.7%
5 722652
 
6.6%
3 721318
 
6.6%
T 455206
 
4.2%
Z 455205
 
4.2%
Other values (10) 1634856
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10923062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2078433
19.0%
0 1154714
10.6%
1 1148157
10.5%
- 910410
8.3%
: 910410
8.3%
4 731701
 
6.7%
5 722652
 
6.6%
3 721318
 
6.6%
T 455206
 
4.2%
Z 455205
 
4.2%
Other values (10) 1634856
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10923062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2078433
19.0%
0 1154714
10.6%
1 1148157
10.5%
- 910410
8.3%
: 910410
8.3%
4 731701
 
6.7%
5 722652
 
6.6%
3 721318
 
6.6%
T 455206
 
4.2%
Z 455205
 
4.2%
Other values (10) 1634856
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10923062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2078433
19.0%
0 1154714
10.6%
1 1148157
10.5%
- 910410
8.3%
: 910410
8.3%
4 731701
 
6.7%
5 722652
 
6.6%
3 721318
 
6.6%
T 455206
 
4.2%
Z 455205
 
4.2%
Other values (10) 1634856
15.0%

elevation
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

elevationAccuracy
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

depth
Real number (ℝ)

Missing 

Distinct3043
Distinct (%)1.5%
Missing246174
Missing (%)54.1%
Infinite0
Infinite (%)0.0%
Mean140.6780436
Minimum0
Maximum7949
Zeros4521
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:27.613323image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.45
Q12
median11
Q382.5
95-th percentile732
Maximum7949
Range7949
Interquartile range (IQR)80.5

Descriptive statistics

Standard deviation378.101056
Coefficient of variation (CV)2.68770482
Kurtosis56.27031611
Mean140.6780436
Median Absolute Deviation (MAD)10.5
Skewness6.070704326
Sum29407056.89
Variance142960.4085
MonotonicityNot monotonic
2025-01-02T17:09:27.688426image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 14691
 
3.2%
1 9522
 
2.1%
1.5 7688
 
1.7%
3 5171
 
1.1%
4 5073
 
1.1%
2.5 5043
 
1.1%
0 4521
 
1.0%
2 4468
 
1.0%
3.5 4405
 
1.0%
5 4020
 
0.9%
Other values (3033) 144436
31.7%
(Missing) 246174
54.1%
ValueCountFrequency (%)
0 4521
1.0%
0.01 9
 
< 0.1%
0.025 1
 
< 0.1%
0.04 22
 
< 0.1%
0.05 105
 
< 0.1%
ValueCountFrequency (%)
7949 1
< 0.1%
7907 1
< 0.1%
7841 2
< 0.1%
7652 2
< 0.1%
7626 2
< 0.1%

depthAccuracy
Real number (ℝ)

Missing  Zeros 

Distinct1170
Distinct (%)0.6%
Missing266866
Missing (%)58.6%
Infinite0
Infinite (%)0.0%
Mean34.32965656
Minimum0
Maximum3741.5
Zeros39321
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:27.753202image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.45
median1.5
Q35.25
95-th percentile185
Maximum3741.5
Range3741.5
Interquartile range (IQR)4.8

Descriptive statistics

Standard deviation144.7176803
Coefficient of variation (CV)4.21552951
Kurtosis91.18419629
Mean34.32965656
Median Absolute Deviation (MAD)1.5
Skewness8.074048197
Sum6465853.495
Variance20943.20699
MonotonicityNot monotonic
2025-01-02T17:09:27.823279image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 39321
 
8.6%
0.5 19670
 
4.3%
1.5 14947
 
3.3%
1 14431
 
3.2%
2.5 8360
 
1.8%
2 7760
 
1.7%
3 7373
 
1.6%
5 4367
 
1.0%
3.5 3758
 
0.8%
0.25 3416
 
0.8%
Other values (1160) 64943
 
14.3%
(Missing) 266866
58.6%
ValueCountFrequency (%)
0 39321
8.6%
0.01 12
 
< 0.1%
0.025 36
 
< 0.1%
0.025 39
 
< 0.1%
0.03 1
 
< 0.1%
ValueCountFrequency (%)
3741.5 1
 
< 0.1%
3600 1
 
< 0.1%
2758.5 3
 
< 0.1%
2706.5 16
< 0.1%
2646 7
< 0.1%

distanceFromCentroidInMeters
Real number (ℝ)

Missing 

Distinct43
Distinct (%)4.7%
Missing454306
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean3630.37456
Minimum645.9027477
Maximum4954.93003
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:27.890701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum645.9027477
5-th percentile1914.901062
Q13413.247522
median3997.886559
Q34049.579333
95-th percentile4756.702967
Maximum4954.93003
Range4309.027282
Interquartile range (IQR)636.3318109

Descriptive statistics

Standard deviation836.4929569
Coefficient of variation (CV)0.2304150558
Kurtosis0.6633441611
Mean3630.37456
Median Absolute Deviation (MAD)528.5707052
Skewness-1.074706803
Sum3289119.351
Variance699720.4669
MonotonicityNot monotonic
2025-01-02T17:09:27.954024image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3997.886559 149
 
< 0.1%
1914.901062 85
 
< 0.1%
4049.579333 75
 
< 0.1%
3435.299369 74
 
< 0.1%
4315.889421 72
 
< 0.1%
3469.315854 51
 
< 0.1%
3286.338393 50
 
< 0.1%
3413.247522 44
 
< 0.1%
3868.839759 35
 
< 0.1%
4088.010727 28
 
< 0.1%
Other values (33) 243
 
0.1%
(Missing) 454306
99.8%
ValueCountFrequency (%)
645.9027477 1
 
< 0.1%
918.1358065 1
 
< 0.1%
1249.870119 12
 
< 0.1%
1914.901062 85
< 0.1%
2023.339547 22
 
< 0.1%
ValueCountFrequency (%)
4954.93003 18
< 0.1%
4813.912657 21
< 0.1%
4811.068347 1
 
< 0.1%
4756.702967 13
< 0.1%
4751.654303 10
< 0.1%

issue
Text

Distinct224
Distinct (%)< 0.1%
Missing15
Missing (%)< 0.1%
Memory size3.5 MiB
2025-01-02T17:09:28.034831image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length213
Median length208
Mean length86.91829032
Min length46

Characters and Unicode

Total characters39564945
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)< 0.1%

Sample

1st rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT
2nd rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT
3rd rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT
4th rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT
5th rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT
ValueCountFrequency (%)
occurrence_status_inferred_from_individual_count 145701
32.0%
occurrence_status_inferred_from_individual_count;continent_derived_from_country;continent_invalid 90868
20.0%
occurrence_status_inferred_from_individual_count;geodetic_datum_assumed_wgs84;continent_invalid 74492
16.4%
occurrence_status_inferred_from_individual_count;geodetic_datum_assumed_wgs84;continent_derived_from_coordinates;continent_invalid 73665
16.2%
occurrence_status_inferred_from_individual_count;geodetic_datum_assumed_wgs84 23814
 
5.2%
occurrence_status_inferred_from_individual_count;geodetic_datum_assumed_wgs84;geodetic_datum_invalid;continent_derived_from_coordinates;continent_invalid 5912
 
1.3%
occurrence_status_inferred_from_individual_count;country_derived_from_coordinates;geodetic_datum_assumed_wgs84;continent_invalid 4969
 
1.1%
occurrence_status_inferred_from_individual_count;country_coordinate_mismatch;geodetic_datum_assumed_wgs84;continent_invalid 4544
 
1.0%
occurrence_status_inferred_from_individual_count;taxon_match_higherrank 3432
 
0.8%
occurrence_status_inferred_from_individual_count;taxon_match_fuzzy 2582
 
0.6%
Other values (214) 25218
 
5.5%
2025-01-02T17:09:28.195784image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 3794873
9.6%
N 3679859
9.3%
E 3402744
 
8.6%
I 3339442
 
8.4%
T 2939907
 
7.4%
R 2888137
 
7.3%
D 2760112
 
7.0%
C 2717539
 
6.9%
O 2539725
 
6.4%
U 2340457
 
5.9%
Other values (18) 9162150
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39564945
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 3794873
9.6%
N 3679859
9.3%
E 3402744
 
8.6%
I 3339442
 
8.4%
T 2939907
 
7.4%
R 2888137
 
7.3%
D 2760112
 
7.0%
C 2717539
 
6.9%
O 2539725
 
6.4%
U 2340457
 
5.9%
Other values (18) 9162150
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39564945
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 3794873
9.6%
N 3679859
9.3%
E 3402744
 
8.6%
I 3339442
 
8.4%
T 2939907
 
7.4%
R 2888137
 
7.3%
D 2760112
 
7.0%
C 2717539
 
6.9%
O 2539725
 
6.4%
U 2340457
 
5.9%
Other values (18) 9162150
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39564945
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 3794873
9.6%
N 3679859
9.3%
E 3402744
 
8.6%
I 3339442
 
8.4%
T 2939907
 
7.4%
R 2888137
 
7.3%
D 2760112
 
7.0%
C 2717539
 
6.9%
O 2539725
 
6.4%
U 2340457
 
5.9%
Other values (18) 9162150
23.2%

mediaType
Text

Missing 

Distinct34
Distinct (%)< 0.1%
Missing363819
Missing (%)79.9%
Memory size3.5 MiB
2025-01-02T17:09:28.261325image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length659
Median length10
Mean length17.04920508
Min length10

Characters and Unicode

Total characters1558178
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowStillImage
2nd rowStillImage
3rd rowStillImage
4th rowStillImage
5th rowStillImage
ValueCountFrequency (%)
stillimage 60095
65.8%
stillimage;stillimage 16175
 
17.7%
stillimage;stillimage;stillimage 9136
 
10.0%
stillimage;stillimage;stillimage;stillimage 3567
 
3.9%
stillimage;stillimage;stillimage;stillimage;stillimage 1344
 
1.5%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 427
 
0.5%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 208
 
0.2%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 113
 
0.1%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 88
 
0.1%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 65
 
0.1%
Other values (24) 175
 
0.2%
2025-01-02T17:09:28.399087image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 299922
19.2%
S 149961
9.6%
t 149961
9.6%
i 149961
9.6%
I 149961
9.6%
m 149961
9.6%
a 149961
9.6%
g 149961
9.6%
e 149961
9.6%
; 58568
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1558178
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 299922
19.2%
S 149961
9.6%
t 149961
9.6%
i 149961
9.6%
I 149961
9.6%
m 149961
9.6%
a 149961
9.6%
g 149961
9.6%
e 149961
9.6%
; 58568
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1558178
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 299922
19.2%
S 149961
9.6%
t 149961
9.6%
i 149961
9.6%
I 149961
9.6%
m 149961
9.6%
a 149961
9.6%
g 149961
9.6%
e 149961
9.6%
; 58568
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1558178
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 299922
19.2%
S 149961
9.6%
t 149961
9.6%
i 149961
9.6%
I 149961
9.6%
m 149961
9.6%
a 149961
9.6%
g 149961
9.6%
e 149961
9.6%
; 58568
 
3.8%
Distinct2
Distinct (%)< 0.1%
Missing7
Missing (%)< 0.1%
Memory size3.5 MiB
False
254250 
True
200955 
(Missing)
 
7
ValueCountFrequency (%)
False 254250
55.9%
True 200955
44.1%
(Missing) 7
 
< 0.1%
2025-01-02T17:09:28.447284image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

hasGeospatialIssues
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing7
Missing (%)< 0.1%
Memory size3.5 MiB
False
449070 
True
 
6135
(Missing)
 
7
ValueCountFrequency (%)
False 449070
98.7%
True 6135
 
1.3%
(Missing) 7
 
< 0.1%
2025-01-02T17:09:28.488515image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

taxonKey
Real number (ℝ)

Distinct28364
Distinct (%)6.2%
Missing7
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2992263.732
Minimum0
Maximum12384557
Zeros204
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:28.539720image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9670
Q12366968
median2391252
Q32412295
95-th percentile6071054
Maximum12384557
Range12384557
Interquartile range (IQR)45327

Descriptive statistics

Standard deviation1723731.457
Coefficient of variation (CV)0.5760626776
Kurtosis4.518052894
Mean2992263.732
Median Absolute Deviation (MAD)22608
Skewness1.806681348
Sum1.362093412 × 1012
Variance2.971250137 × 1012
MonotonicityNot monotonic
2025-01-02T17:09:28.610330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4274 1630
 
0.4%
2376138 1001
 
0.2%
2359014 971
 
0.2%
2360481 895
 
0.2%
2367736 889
 
0.2%
2361357 853
 
0.2%
2359823 815
 
0.2%
2358931 758
 
0.2%
2365441 757
 
0.2%
4253 730
 
0.2%
Other values (28354) 445906
98.0%
ValueCountFrequency (%)
0 204
< 0.1%
44 198
< 0.1%
121 2
 
< 0.1%
495 140
< 0.1%
537 13
 
< 0.1%
ValueCountFrequency (%)
12384557 2
< 0.1%
12373851 1
 
< 0.1%
12368448 1
 
< 0.1%
12353108 4
< 0.1%
12350334 1
 
< 0.1%

acceptedTaxonKey
Real number (ℝ)

Distinct22054
Distinct (%)4.8%
Missing211
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2959189.521
Minimum44
Maximum12381359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:28.786584image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile8612
Q12365449
median2391041
Q32411792
95-th percentile5853528
Maximum12381359
Range12381315
Interquartile range (IQR)46343

Descriptive statistics

Standard deviation1683886.377
Coefficient of variation (CV)0.5690363408
Kurtosis4.277211668
Mean2959189.521
Median Absolute Deviation (MAD)22393
Skewness1.765985661
Sum1.346434191 × 1012
Variance2.83547333 × 1012
MonotonicityNot monotonic
2025-01-02T17:09:28.861444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4274 1630
 
0.4%
2360481 1121
 
0.2%
2359014 1113
 
0.2%
2359823 1006
 
0.2%
2376138 1001
 
0.2%
2366967 904
 
0.2%
2367736 893
 
0.2%
2394503 857
 
0.2%
2361357 853
 
0.2%
2365441 760
 
0.2%
Other values (22044) 444863
97.7%
ValueCountFrequency (%)
44 198
< 0.1%
121 2
 
< 0.1%
495 140
< 0.1%
537 13
 
< 0.1%
547 5
 
< 0.1%
ValueCountFrequency (%)
12381359 13
< 0.1%
12322426 1
 
< 0.1%
12306692 3
 
< 0.1%
12290919 1
 
< 0.1%
12283882 8
< 0.1%

kingdomKey
Real number (ℝ)

Skewed 

Distinct2
Distinct (%)< 0.1%
Missing7
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.9995452598
Minimum0
Maximum1
Zeros207
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:28.917023image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.02131981196
Coefficient of variation (CV)0.02132951134
Kurtosis2194.082539
Mean0.9995452598
Median Absolute Deviation (MAD)0
Skewness-46.86227586
Sum454998
Variance0.0004545343819
MonotonicityNot monotonic
2025-01-02T17:09:28.964546image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 454998
> 99.9%
0 207
 
< 0.1%
(Missing) 7
 
< 0.1%
ValueCountFrequency (%)
0 207
 
< 0.1%
1 454998
> 99.9%
ValueCountFrequency (%)
1 454998
> 99.9%
0 207
 
< 0.1%

phylumKey
Real number (ℝ)

Skewed 

Distinct2
Distinct (%)< 0.1%
Missing292
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean44.00015387
Minimum44
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:29.011955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile44
Q144
median44
Q344
95-th percentile44
Maximum54
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.03922641699
Coefficient of variation (CV)0.0008915063594
Kurtosis64984.28569
Mean44.00015387
Median Absolute Deviation (MAD)0
Skewness254.9235179
Sum20016550
Variance0.00153871179
MonotonicityNot monotonic
2025-01-02T17:09:29.059242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
44 454913
99.9%
54 7
 
< 0.1%
(Missing) 292
 
0.1%
ValueCountFrequency (%)
44 454913
99.9%
54 7
 
< 0.1%
ValueCountFrequency (%)
54 7
 
< 0.1%
44 454913
99.9%

classKey
Real number (ℝ)

Missing 

Distinct9
Distinct (%)0.1%
Missing444746
Missing (%)97.7%
Infinite0
Infinite (%)0.0%
Mean1050196.463
Minimum119
Maximum11881065
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:29.105846image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum119
5-th percentile121
Q1121
median121
Q3121
95-th percentile11881065
Maximum11881065
Range11880946
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3119003.736
Coefficient of variation (CV)2.969924054
Kurtosis6.218663003
Mean1050196.463
Median Absolute Deviation (MAD)0
Skewness2.791304644
Sum1.099135618 × 1010
Variance9.728184305 × 1012
MonotonicityNot monotonic
2025-01-02T17:09:29.156429image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
121 8825
 
1.9%
11881065 565
 
0.1%
7375758 514
 
0.1%
120 362
 
0.1%
119 150
 
< 0.1%
11500725 28
 
< 0.1%
11733052 14
 
< 0.1%
367 7
 
< 0.1%
131 1
 
< 0.1%
(Missing) 444746
97.7%
ValueCountFrequency (%)
119 150
 
< 0.1%
120 362
 
0.1%
121 8825
1.9%
131 1
 
< 0.1%
367 7
 
< 0.1%
ValueCountFrequency (%)
11881065 565
0.1%
11733052 14
 
< 0.1%
11500725 28
 
< 0.1%
7375758 514
0.1%
367 7
 
< 0.1%

orderKey
Real number (ℝ)

Distinct64
Distinct (%)< 0.1%
Missing1008
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean26995.48461
Minimum494
Maximum9412443
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:29.216897image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum494
5-th percentile497
Q1587
median587
Q3708
95-th percentile1306
Maximum9412443
Range9411949
Interquartile range (IQR)121

Descriptive statistics

Standard deviation472505.1701
Coefficient of variation (CV)17.50311865
Kurtosis322.4345911
Mean26995.48461
Median Absolute Deviation (MAD)3
Skewness17.97605386
Sum1.226145709 × 1010
Variance2.232611358 × 1011
MonotonicityNot monotonic
2025-01-02T17:09:29.290613image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
587 212582
46.7%
1153 33752
 
7.4%
590 17672
 
3.9%
537 17478
 
3.8%
495 17113
 
3.8%
708 14280
 
3.1%
1306 13708
 
3.0%
588 12320
 
2.7%
774 12085
 
2.7%
772 10526
 
2.3%
Other values (54) 92688
20.4%
ValueCountFrequency (%)
494 75
 
< 0.1%
495 17113
3.8%
496 4168
 
0.9%
497 7127
1.6%
498 9108
2.0%
ValueCountFrequency (%)
9412443 319
 
0.1%
8214029 1089
 
0.2%
1496 7
 
< 0.1%
1313 3952
0.9%
1312 111
 
< 0.1%

familyKey
Real number (ℝ)

Distinct554
Distinct (%)0.1%
Missing840
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean49985.25313
Minimum2178
Maximum11163701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:29.354197image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2178
5-th percentile2227
Q14251
median5081
Q38050
95-th percentile8612
Maximum11163701
Range11161523
Interquartile range (IQR)3799

Descriptive statistics

Standard deviation527604.7851
Coefficient of variation (CV)10.55520883
Kurtosis155.8874048
Mean49985.25313
Median Absolute Deviation (MAD)2114
Skewness12.36156785
Sum2.271189944 × 1010
Variance2.783668092 × 1011
MonotonicityNot monotonic
2025-01-02T17:09:29.424461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7336 27640
 
6.1%
4274 26017
 
5.7%
4499 16208
 
3.6%
8535 14638
 
3.2%
4251 14508
 
3.2%
4217 13553
 
3.0%
4236 12381
 
2.7%
8597 11376
 
2.5%
7201 9124
 
2.0%
2225 7881
 
1.7%
Other values (544) 301046
66.1%
ValueCountFrequency (%)
2178 17
 
< 0.1%
2180 54
 
< 0.1%
2181 258
0.1%
2182 50
 
< 0.1%
2183 148
< 0.1%
ValueCountFrequency (%)
11163701 1
 
< 0.1%
9904396 2
 
< 0.1%
9620830 31
< 0.1%
9470195 1
 
< 0.1%
9361873 17
< 0.1%

genusKey
Real number (ℝ)

Missing 

Distinct4426
Distinct (%)1.0%
Missing23593
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean2647027.83
Minimum1011633
Maximum12322426
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:29.492389image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1011633
5-th percentile2336274
Q12360060
median2382199
Q32401859
95-th percentile5203220
Maximum12322426
Range11310793
Interquartile range (IQR)41799

Descriptive statistics

Standard deviation1121406.818
Coefficient of variation (CV)0.4236475359
Kurtosis22.74279858
Mean2647027.83
Median Absolute Deviation (MAD)20764
Skewness4.673719713
Sum1.142507505 × 1012
Variance1.257553251 × 1012
MonotonicityNot monotonic
2025-01-02T17:09:29.559523image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2382199 5026
 
1.1%
2403463 4350
 
1.0%
2394482 4347
 
1.0%
2362128 4334
 
1.0%
2369550 4239
 
0.9%
2356953 3825
 
0.8%
2381823 3118
 
0.7%
5962165 3031
 
0.7%
2379647 2923
 
0.6%
2380069 2919
 
0.6%
Other values (4416) 393507
86.4%
(Missing) 23593
 
5.2%
ValueCountFrequency (%)
1011633 1
 
< 0.1%
1347342 1
 
< 0.1%
2075899 7
 
< 0.1%
2226042 11
 
< 0.1%
2332594 231
0.1%
ValueCountFrequency (%)
12322426 1
 
< 0.1%
12306692 3
 
< 0.1%
12266770 5
< 0.1%
12265559 7
< 0.1%
12197768 9
< 0.1%

subgenusKey
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

speciesKey
Real number (ℝ)

Missing 

Distinct19431
Distinct (%)5.0%
Missing70260
Missing (%)15.4%
Infinite0
Infinite (%)0.0%
Mean3147437.511
Minimum2332595
Maximum12290919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2025-01-02T17:09:29.630148image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2332595
5-th percentile2341021
Q12374898
median2394521
Q32415215
95-th percentile5853522
Maximum12290919
Range9958324
Interquartile range (IQR)40317

Descriptive statistics

Standard deviation1572655.285
Coefficient of variation (CV)0.4996621155
Kurtosis4.78988809
Mean3147437.511
Median Absolute Deviation (MAD)19759
Skewness2.195953271
Sum1.211612365 × 1012
Variance2.473244646 × 1012
MonotonicityNot monotonic
2025-01-02T17:09:29.700733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2360481 1121
 
0.2%
2359014 1113
 
0.2%
2359823 1006
 
0.2%
2361357 943
 
0.2%
2365439 938
 
0.2%
2366967 904
 
0.2%
2367736 893
 
0.2%
2394503 857
 
0.2%
2365441 760
 
0.2%
2358931 760
 
0.2%
Other values (19421) 375657
82.5%
(Missing) 70260
 
15.4%
ValueCountFrequency (%)
2332595 93
< 0.1%
2332598 4
 
< 0.1%
2332603 109
< 0.1%
2332630 2
 
< 0.1%
2332631 1
 
< 0.1%
ValueCountFrequency (%)
12290919 1
 
< 0.1%
12283882 8
< 0.1%
12265834 1
 
< 0.1%
12265146 2
 
< 0.1%
12262503 1
 
< 0.1%

species
Text

Missing 

Distinct19429
Distinct (%)5.0%
Missing70260
Missing (%)15.4%
Memory size3.5 MiB
2025-01-02T17:09:29.820414image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length35
Median length31
Mean length19.79614082
Min length8

Characters and Unicode

Total characters7620564
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4217 ?
Unique (%)1.1%

Sample

1st rowEchidna nebulosa
2nd rowMyersina filifer
3rd rowRhinichthys cataractae
4th rowCentropomus ensiferus
5th rowGorgasia inferomaculata
ValueCountFrequency (%)
etheostoma 4924
 
0.6%
chaetodon 4170
 
0.5%
notropis 4110
 
0.5%
lepomis 4038
 
0.5%
gymnothorax 4025
 
0.5%
lutjanus 3782
 
0.5%
chromis 2870
 
0.4%
halichoeres 2861
 
0.4%
synodus 2680
 
0.3%
acanthurus 2550
 
0.3%
Other values (15283) 733894
95.3%
2025-01-02T17:09:30.011471image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 711716
 
9.3%
a 681062
 
8.9%
i 624744
 
8.2%
o 551041
 
7.2%
u 497264
 
6.5%
e 491012
 
6.4%
r 459068
 
6.0%
t 435608
 
5.7%
n 408570
 
5.4%
384952
 
5.1%
Other values (44) 2375527
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7620564
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 711716
 
9.3%
a 681062
 
8.9%
i 624744
 
8.2%
o 551041
 
7.2%
u 497264
 
6.5%
e 491012
 
6.4%
r 459068
 
6.0%
t 435608
 
5.7%
n 408570
 
5.4%
384952
 
5.1%
Other values (44) 2375527
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7620564
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 711716
 
9.3%
a 681062
 
8.9%
i 624744
 
8.2%
o 551041
 
7.2%
u 497264
 
6.5%
e 491012
 
6.4%
r 459068
 
6.0%
t 435608
 
5.7%
n 408570
 
5.4%
384952
 
5.1%
Other values (44) 2375527
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7620564
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 711716
 
9.3%
a 681062
 
8.9%
i 624744
 
8.2%
o 551041
 
7.2%
u 497264
 
6.5%
e 491012
 
6.4%
r 459068
 
6.0%
t 435608
 
5.7%
n 408570
 
5.4%
384952
 
5.1%
Other values (44) 2375527
31.2%
Distinct22054
Distinct (%)4.8%
Missing211
Missing (%)< 0.1%
Memory size3.5 MiB
2025-01-02T17:09:30.147067image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length111
Median length88
Mean length34.4021156
Min length7

Characters and Unicode

Total characters15652997
Distinct characters90
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4768 ?
Unique (%)1.0%

Sample

1st rowEchidna nebulosa (Ahl, 1789)
2nd rowMugil Linnaeus, 1758
3rd rowMyersina filifer (Valenciennes, 1837)
4th rowRhinichthys cataractae (Valenciennes, 1842)
5th rowCentropomus ensiferus Poey, 1860
ValueCountFrequency (%)
73674
 
4.0%
linnaeus 27502
 
1.5%
bleeker 24276
 
1.3%
1758 21716
 
1.2%
valenciennes 20911
 
1.1%
cuvier 18805
 
1.0%
bloch 16076
 
0.9%
jordan 16047
 
0.9%
lacepède 14474
 
0.8%
günther 13679
 
0.7%
Other values (18045) 1608922
86.7%
2025-01-02T17:09:30.353142image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1401081
 
9.0%
e 1049108
 
6.7%
a 1030592
 
6.6%
i 924453
 
5.9%
s 912612
 
5.8%
n 776988
 
5.0%
r 763435
 
4.9%
o 760269
 
4.9%
u 639070
 
4.1%
l 589065
 
3.8%
Other values (80) 6806324
43.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15652997
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1401081
 
9.0%
e 1049108
 
6.7%
a 1030592
 
6.6%
i 924453
 
5.9%
s 912612
 
5.8%
n 776988
 
5.0%
r 763435
 
4.9%
o 760269
 
4.9%
u 639070
 
4.1%
l 589065
 
3.8%
Other values (80) 6806324
43.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15652997
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1401081
 
9.0%
e 1049108
 
6.7%
a 1030592
 
6.6%
i 924453
 
5.9%
s 912612
 
5.8%
n 776988
 
5.0%
r 763435
 
4.9%
o 760269
 
4.9%
u 639070
 
4.1%
l 589065
 
3.8%
Other values (80) 6806324
43.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15652997
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1401081
 
9.0%
e 1049108
 
6.7%
a 1030592
 
6.6%
i 924453
 
5.9%
s 912612
 
5.8%
n 776988
 
5.0%
r 763435
 
4.9%
o 760269
 
4.9%
u 639070
 
4.1%
l 589065
 
3.8%
Other values (80) 6806324
43.5%
Distinct30202
Distinct (%)6.6%
Missing14
Missing (%)< 0.1%
Memory size3.5 MiB
2025-01-02T17:09:30.488533image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length69
Median length54
Mean length18.57410402
Min length2

Characters and Unicode

Total characters8454895
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9327 ?
Unique (%)2.0%

Sample

1st rowEchidna nebulosa
2nd rowMugil
3rd rowCryptocentrus filifer
4th rowRhinichthys cataractae
5th rowCentropomus ensiferus
ValueCountFrequency (%)
notropis 7207
 
0.8%
etheostoma 4890
 
0.6%
chaetodon 4339
 
0.5%
gymnothorax 4324
 
0.5%
lepomis 4273
 
0.5%
lutjanus 3888
 
0.5%
chromis 3151
 
0.4%
halichoeres 3126
 
0.4%
pomacentrus 2957
 
0.3%
acanthurus 2893
 
0.3%
Other values (18882) 813929
95.2%
2025-01-02T17:09:30.709476image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 773325
 
9.1%
a 769363
 
9.1%
i 700851
 
8.3%
o 618984
 
7.3%
e 559438
 
6.6%
u 535543
 
6.3%
r 508516
 
6.0%
t 479216
 
5.7%
n 446288
 
5.3%
399779
 
4.7%
Other values (60) 2663592
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8454895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 773325
 
9.1%
a 769363
 
9.1%
i 700851
 
8.3%
o 618984
 
7.3%
e 559438
 
6.6%
u 535543
 
6.3%
r 508516
 
6.0%
t 479216
 
5.7%
n 446288
 
5.3%
399779
 
4.7%
Other values (60) 2663592
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8454895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 773325
 
9.1%
a 769363
 
9.1%
i 700851
 
8.3%
o 618984
 
7.3%
e 559438
 
6.6%
u 535543
 
6.3%
r 508516
 
6.0%
t 479216
 
5.7%
n 446288
 
5.3%
399779
 
4.7%
Other values (60) 2663592
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8454895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 773325
 
9.1%
a 769363
 
9.1%
i 700851
 
8.3%
o 618984
 
7.3%
e 559438
 
6.6%
u 535543
 
6.3%
r 508516
 
6.0%
t 479216
 
5.7%
n 446288
 
5.3%
399779
 
4.7%
Other values (60) 2663592
31.5%

typifiedName
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

protocol
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing7
Missing (%)< 0.1%
Memory size3.5 MiB
2025-01-02T17:09:30.756496image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1365615
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEML
2nd rowEML
3rd rowEML
4th rowEML
5th rowEML
ValueCountFrequency (%)
eml 455205
100.0%
2025-01-02T17:09:30.846208image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 455205
33.3%
M 455205
33.3%
L 455205
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1365615
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 455205
33.3%
M 455205
33.3%
L 455205
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1365615
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 455205
33.3%
M 455205
33.3%
L 455205
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1365615
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 455205
33.3%
M 455205
33.3%
L 455205
33.3%
Distinct173323
Distinct (%)38.1%
Missing7
Missing (%)< 0.1%
Memory size3.5 MiB
2025-01-02T17:09:30.978519image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length23.99588757
Min length20

Characters and Unicode

Total characters10923048
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50645 ?
Unique (%)11.1%

Sample

1st row2024-12-02T13:56:09.099Z
2nd row2024-12-02T13:56:08.596Z
3rd row2024-12-02T13:59:51.375Z
4th row2024-12-02T13:58:24.571Z
5th row2024-12-02T13:56:08.212Z
ValueCountFrequency (%)
2024-12-02t13:57:01.873z 14
 
< 0.1%
2024-12-02t13:57:53.333z 14
 
< 0.1%
2024-12-02t13:57:04.016z 13
 
< 0.1%
2024-12-02t13:58:01.465z 13
 
< 0.1%
2024-12-02t13:57:52.916z 13
 
< 0.1%
2024-12-02t13:57:28.109z 13
 
< 0.1%
2024-12-02t13:57:41.128z 13
 
< 0.1%
2024-12-02t13:57:03.178z 13
 
< 0.1%
2024-12-02t13:57:30.416z 13
 
< 0.1%
2024-12-02t13:58:01.584z 12
 
< 0.1%
Other values (173313) 455074
> 99.9%
2025-01-02T17:09:31.185239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2078433
19.0%
0 1154714
10.6%
1 1148157
10.5%
- 910410
8.3%
: 910410
8.3%
4 731701
 
6.7%
5 722652
 
6.6%
3 721318
 
6.6%
T 455205
 
4.2%
Z 455205
 
4.2%
Other values (5) 1634843
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10923048
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2078433
19.0%
0 1154714
10.6%
1 1148157
10.5%
- 910410
8.3%
: 910410
8.3%
4 731701
 
6.7%
5 722652
 
6.6%
3 721318
 
6.6%
T 455205
 
4.2%
Z 455205
 
4.2%
Other values (5) 1634843
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10923048
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2078433
19.0%
0 1154714
10.6%
1 1148157
10.5%
- 910410
8.3%
: 910410
8.3%
4 731701
 
6.7%
5 722652
 
6.6%
3 721318
 
6.6%
T 455205
 
4.2%
Z 455205
 
4.2%
Other values (5) 1634843
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10923048
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2078433
19.0%
0 1154714
10.6%
1 1148157
10.5%
- 910410
8.3%
: 910410
8.3%
4 731701
 
6.7%
5 722652
 
6.6%
3 721318
 
6.6%
T 455205
 
4.2%
Z 455205
 
4.2%
Other values (5) 1634843
15.0%

lastCrawled
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing7
Missing (%)< 0.1%
Memory size3.5 MiB
2025-01-02T17:09:31.259286image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters10924920
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-12-02T11:48:23.416Z
2nd row2024-12-02T11:48:23.416Z
3rd row2024-12-02T11:48:23.416Z
4th row2024-12-02T11:48:23.416Z
5th row2024-12-02T11:48:23.416Z
ValueCountFrequency (%)
2024-12-02t11:48:23.416z 455205
100.0%
2025-01-02T17:09:31.481870image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2276025
20.8%
1 1820820
16.7%
4 1365615
12.5%
0 910410
 
8.3%
- 910410
 
8.3%
: 910410
 
8.3%
T 455205
 
4.2%
8 455205
 
4.2%
3 455205
 
4.2%
. 455205
 
4.2%
Other values (2) 910410
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10924920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2276025
20.8%
1 1820820
16.7%
4 1365615
12.5%
0 910410
 
8.3%
- 910410
 
8.3%
: 910410
 
8.3%
T 455205
 
4.2%
8 455205
 
4.2%
3 455205
 
4.2%
. 455205
 
4.2%
Other values (2) 910410
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10924920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2276025
20.8%
1 1820820
16.7%
4 1365615
12.5%
0 910410
 
8.3%
- 910410
 
8.3%
: 910410
 
8.3%
T 455205
 
4.2%
8 455205
 
4.2%
3 455205
 
4.2%
. 455205
 
4.2%
Other values (2) 910410
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10924920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2276025
20.8%
1 1820820
16.7%
4 1365615
12.5%
0 910410
 
8.3%
- 910410
 
8.3%
: 910410
 
8.3%
T 455205
 
4.2%
8 455205
 
4.2%
3 455205
 
4.2%
. 455205
 
4.2%
Other values (2) 910410
 
8.3%

repatriated
Boolean

Missing 

Distinct2
Distinct (%)< 0.1%
Missing30397
Missing (%)6.7%
Memory size3.5 MiB
True
300251 
False
124564 
(Missing)
30397 
ValueCountFrequency (%)
True 300251
66.0%
False 124564
27.4%
(Missing) 30397
 
6.7%
2025-01-02T17:09:31.534650image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

relativeOrganismQuantity
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

projectId
Unsupported

Missing  Rejected  Unsupported 

Missing455212
Missing (%)100.0%
Memory size3.5 MiB

isSequenced
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing7
Missing (%)< 0.1%
Memory size3.5 MiB
False
454755 
True
 
450
(Missing)
 
7
ValueCountFrequency (%)
False 454755
99.9%
True 450
 
0.1%
(Missing) 7
 
< 0.1%
2025-01-02T17:09:31.577526image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

gbifRegion
Text

Missing 

Distinct7
Distinct (%)< 0.1%
Missing32195
Missing (%)7.1%
Memory size3.5 MiB
2025-01-02T17:09:31.632424image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length9.506102592
Min length4

Characters and Unicode

Total characters4021243
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORTH_AMERICA
2nd rowNORTH_AMERICA
3rd rowASIA
4th rowNORTH_AMERICA
5th rowLATIN_AMERICA
ValueCountFrequency (%)
north_america 127654
30.2%
latin_america 100745
23.8%
asia 94416
22.3%
oceania 68048
16.1%
africa 24873
 
5.9%
europe 5998
 
1.4%
antarctica 1283
 
0.3%
2025-01-02T17:09:31.753730image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 936066
23.3%
I 517764
12.9%
R 388207
9.7%
C 323886
 
8.1%
E 308443
 
7.7%
N 297730
 
7.4%
T 230965
 
5.7%
M 228399
 
5.7%
_ 228399
 
5.7%
O 201700
 
5.0%
Other values (6) 359684
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4021243
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 936066
23.3%
I 517764
12.9%
R 388207
9.7%
C 323886
 
8.1%
E 308443
 
7.7%
N 297730
 
7.4%
T 230965
 
5.7%
M 228399
 
5.7%
_ 228399
 
5.7%
O 201700
 
5.0%
Other values (6) 359684
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4021243
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 936066
23.3%
I 517764
12.9%
R 388207
9.7%
C 323886
 
8.1%
E 308443
 
7.7%
N 297730
 
7.4%
T 230965
 
5.7%
M 228399
 
5.7%
_ 228399
 
5.7%
O 201700
 
5.0%
Other values (6) 359684
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4021243
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 936066
23.3%
I 517764
12.9%
R 388207
9.7%
C 323886
 
8.1%
E 308443
 
7.7%
N 297730
 
7.4%
T 230965
 
5.7%
M 228399
 
5.7%
_ 228399
 
5.7%
O 201700
 
5.0%
Other values (6) 359684
 
8.9%

publishedByGbifRegion
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing7
Missing (%)< 0.1%
Memory size3.5 MiB
2025-01-02T17:09:31.820073image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters5917665
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORTH_AMERICA
2nd rowNORTH_AMERICA
3rd rowNORTH_AMERICA
4th rowNORTH_AMERICA
5th rowNORTH_AMERICA
ValueCountFrequency (%)
north_america 455205
100.0%
2025-01-02T17:09:31.938801image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 910410
15.4%
A 910410
15.4%
N 455205
7.7%
O 455205
7.7%
T 455205
7.7%
H 455205
7.7%
_ 455205
7.7%
M 455205
7.7%
E 455205
7.7%
I 455205
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5917665
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 910410
15.4%
A 910410
15.4%
N 455205
7.7%
O 455205
7.7%
T 455205
7.7%
H 455205
7.7%
_ 455205
7.7%
M 455205
7.7%
E 455205
7.7%
I 455205
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5917665
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 910410
15.4%
A 910410
15.4%
N 455205
7.7%
O 455205
7.7%
T 455205
7.7%
H 455205
7.7%
_ 455205
7.7%
M 455205
7.7%
E 455205
7.7%
I 455205
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5917665
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 910410
15.4%
A 910410
15.4%
N 455205
7.7%
O 455205
7.7%
T 455205
7.7%
H 455205
7.7%
_ 455205
7.7%
M 455205
7.7%
E 455205
7.7%
I 455205
7.7%

level0Gid
Text

Missing 

Distinct139
Distinct (%)0.3%
Missing407295
Missing (%)89.5%
Memory size3.5 MiB
2025-01-02T17:09:32.055989image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters143751
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)< 0.1%

Sample

1st rowTON
2nd rowPHL
3rd rowBRA
4th rowPAN
5th rowIDN
ValueCountFrequency (%)
usa 11507
24.0%
phl 5147
 
10.7%
ven 2888
 
6.0%
bra 2826
 
5.9%
idn 2406
 
5.0%
fji 2133
 
4.5%
sur 1264
 
2.6%
per 1237
 
2.6%
png 1100
 
2.3%
slb 1025
 
2.1%
Other values (129) 16384
34.2%
2025-01-02T17:09:32.234165image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 17643
12.3%
S 16829
11.7%
U 15233
 
10.6%
N 10380
 
7.2%
P 9498
 
6.6%
L 8389
 
5.8%
R 7899
 
5.5%
H 6313
 
4.4%
I 5803
 
4.0%
E 5461
 
3.8%
Other values (16) 40303
28.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 143751
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 17643
12.3%
S 16829
11.7%
U 15233
 
10.6%
N 10380
 
7.2%
P 9498
 
6.6%
L 8389
 
5.8%
R 7899
 
5.5%
H 6313
 
4.4%
I 5803
 
4.0%
E 5461
 
3.8%
Other values (16) 40303
28.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 143751
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 17643
12.3%
S 16829
11.7%
U 15233
 
10.6%
N 10380
 
7.2%
P 9498
 
6.6%
L 8389
 
5.8%
R 7899
 
5.5%
H 6313
 
4.4%
I 5803
 
4.0%
E 5461
 
3.8%
Other values (16) 40303
28.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 143751
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 17643
12.3%
S 16829
11.7%
U 15233
 
10.6%
N 10380
 
7.2%
P 9498
 
6.6%
L 8389
 
5.8%
R 7899
 
5.5%
H 6313
 
4.4%
I 5803
 
4.0%
E 5461
 
3.8%
Other values (16) 40303
28.0%

level0Name
Text

Missing 

Distinct139
Distinct (%)0.3%
Missing407295
Missing (%)89.5%
Memory size3.5 MiB
2025-01-02T17:09:32.369452image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length24
Mean length10.19049607
Min length4

Characters and Unicode

Total characters488298
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)< 0.1%

Sample

1st rowTonga
2nd rowPhilippines
3rd rowBrazil
4th rowPanama
5th rowIndonesia
ValueCountFrequency (%)
united 11597
16.7%
states 11597
16.7%
philippines 5147
 
7.4%
venezuela 2888
 
4.2%
brazil 2826
 
4.1%
indonesia 2406
 
3.5%
fiji 2133
 
3.1%
new 1388
 
2.0%
and 1366
 
2.0%
islands 1352
 
1.9%
Other values (173) 26850
38.6%
2025-01-02T17:09:32.571815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 53763
 
11.0%
i 52703
 
10.8%
a 51807
 
10.6%
n 41316
 
8.5%
t 38500
 
7.9%
s 27341
 
5.6%
21633
 
4.4%
d 20330
 
4.2%
l 20222
 
4.1%
S 15363
 
3.1%
Other values (51) 145320
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 488298
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 53763
 
11.0%
i 52703
 
10.8%
a 51807
 
10.6%
n 41316
 
8.5%
t 38500
 
7.9%
s 27341
 
5.6%
21633
 
4.4%
d 20330
 
4.2%
l 20222
 
4.1%
S 15363
 
3.1%
Other values (51) 145320
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 488298
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 53763
 
11.0%
i 52703
 
10.8%
a 51807
 
10.6%
n 41316
 
8.5%
t 38500
 
7.9%
s 27341
 
5.6%
21633
 
4.4%
d 20330
 
4.2%
l 20222
 
4.1%
S 15363
 
3.1%
Other values (51) 145320
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 488298
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 53763
 
11.0%
i 52703
 
10.8%
a 51807
 
10.6%
n 41316
 
8.5%
t 38500
 
7.9%
s 27341
 
5.6%
21633
 
4.4%
d 20330
 
4.2%
l 20222
 
4.1%
S 15363
 
3.1%
Other values (51) 145320
29.8%

level1Gid
Text

Missing 

Distinct629
Distinct (%)1.3%
Missing408402
Missing (%)89.7%
Memory size3.5 MiB
2025-01-02T17:09:32.752199image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.589168981
Min length6

Characters and Unicode

Total characters355249
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique134 ?
Unique (%)0.3%

Sample

1st rowTON.5_1
2nd rowPHL.52_1
3rd rowBRA.13_1
4th rowPAN.5_1
5th rowIDN.12_1
ValueCountFrequency (%)
usa.47_1 2114
 
4.5%
usa.39_1 1909
 
4.1%
usa.21_1 1178
 
2.5%
fji.4_1 1089
 
2.3%
phl.52_1 1010
 
2.2%
sur.9_1 986
 
2.1%
bra.4_1 986
 
2.1%
usa.49_1 966
 
2.1%
fji.2_1 918
 
2.0%
idn.19_1 915
 
2.0%
Other values (619) 34739
74.2%
2025-01-02T17:09:32.996682image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 61632
17.3%
_ 46806
13.2%
. 46779
13.2%
A 17604
 
5.0%
S 16787
 
4.7%
U 14730
 
4.1%
2 12539
 
3.5%
4 10903
 
3.1%
N 10380
 
2.9%
3 9530
 
2.7%
Other values (28) 107559
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 355249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 61632
17.3%
_ 46806
13.2%
. 46779
13.2%
A 17604
 
5.0%
S 16787
 
4.7%
U 14730
 
4.1%
2 12539
 
3.5%
4 10903
 
3.1%
N 10380
 
2.9%
3 9530
 
2.7%
Other values (28) 107559
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 355249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 61632
17.3%
_ 46806
13.2%
. 46779
13.2%
A 17604
 
5.0%
S 16787
 
4.7%
U 14730
 
4.1%
2 12539
 
3.5%
4 10903
 
3.1%
N 10380
 
2.9%
3 9530
 
2.7%
Other values (28) 107559
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 355249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 61632
17.3%
_ 46806
13.2%
. 46779
13.2%
A 17604
 
5.0%
S 16787
 
4.7%
U 14730
 
4.1%
2 12539
 
3.5%
4 10903
 
3.1%
N 10380
 
2.9%
3 9530
 
2.7%
Other values (28) 107559
30.3%

level1Name
Text

Missing 

Distinct611
Distinct (%)1.3%
Missing408402
Missing (%)89.7%
Memory size3.5 MiB
2025-01-02T17:09:33.145225image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length30
Median length24
Mean length9.236039308
Min length3

Characters and Unicode

Total characters432339
Distinct characters82
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique130 ?
Unique (%)0.3%

Sample

1st rowVava'u
2nd rowNegros Oriental
3rd rowMinas Gerais
4th rowDarién
5th rowKalimantan Barat
ValueCountFrequency (%)
virginia 3080
 
4.8%
pennsylvania 1909
 
3.0%
amazonas 1611
 
2.5%
maryland 1178
 
1.8%
south 1090
 
1.7%
rotuma 1089
 
1.7%
islands 1083
 
1.7%
oriental 1050
 
1.6%
negros 1025
 
1.6%
eastern 1020
 
1.6%
Other values (674) 49891
77.9%
2025-01-02T17:09:33.353483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 64833
15.0%
n 33919
 
7.8%
i 32416
 
7.5%
r 26649
 
6.2%
o 26487
 
6.1%
e 26064
 
6.0%
s 19671
 
4.5%
t 19496
 
4.5%
l 19259
 
4.5%
17216
 
4.0%
Other values (72) 146329
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 432339
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 64833
15.0%
n 33919
 
7.8%
i 32416
 
7.5%
r 26649
 
6.2%
o 26487
 
6.1%
e 26064
 
6.0%
s 19671
 
4.5%
t 19496
 
4.5%
l 19259
 
4.5%
17216
 
4.0%
Other values (72) 146329
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 432339
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 64833
15.0%
n 33919
 
7.8%
i 32416
 
7.5%
r 26649
 
6.2%
o 26487
 
6.1%
e 26064
 
6.0%
s 19671
 
4.5%
t 19496
 
4.5%
l 19259
 
4.5%
17216
 
4.0%
Other values (72) 146329
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 432339
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 64833
15.0%
n 33919
 
7.8%
i 32416
 
7.5%
r 26649
 
6.2%
o 26487
 
6.1%
e 26064
 
6.0%
s 19671
 
4.5%
t 19496
 
4.5%
l 19259
 
4.5%
17216
 
4.0%
Other values (72) 146329
33.8%

level2Gid
Text

Missing 

Distinct1834
Distinct (%)4.2%
Missing412023
Missing (%)90.5%
Memory size3.5 MiB
2025-01-02T17:09:33.509746image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.0572368
Min length7

Characters and Unicode

Total characters434362
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique435 ?
Unique (%)1.0%

Sample

1st rowTON.5.0_1
2nd rowPHL.52.17_1
3rd rowBRA.13.511_2
4th rowPAN.5.2_1
5th rowIDN.12.14_1
ValueCountFrequency (%)
fji.4.1_1 1089
 
2.5%
sur.9.5_1 722
 
1.7%
fji.2.2_1 640
 
1.5%
slb.7.26_1 534
 
1.2%
ton.5.0_1 471
 
1.1%
idn.19.1_1 469
 
1.1%
ven.9.1_1 455
 
1.1%
per.17.4_1 448
 
1.0%
idn.19.6_1 444
 
1.0%
idn.28.2_1 443
 
1.0%
Other values (1824) 37474
86.8%
2025-01-02T17:09:33.729780image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 86343
19.9%
1 68426
15.8%
_ 43189
 
9.9%
2 25854
 
6.0%
A 17316
 
4.0%
4 16359
 
3.8%
3 15943
 
3.7%
S 15249
 
3.5%
U 14430
 
3.3%
5 11495
 
2.6%
Other values (28) 119758
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 434362
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 86343
19.9%
1 68426
15.8%
_ 43189
 
9.9%
2 25854
 
6.0%
A 17316
 
4.0%
4 16359
 
3.8%
3 15943
 
3.7%
S 15249
 
3.5%
U 14430
 
3.3%
5 11495
 
2.6%
Other values (28) 119758
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 434362
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 86343
19.9%
1 68426
15.8%
_ 43189
 
9.9%
2 25854
 
6.0%
A 17316
 
4.0%
4 16359
 
3.8%
3 15943
 
3.7%
S 15249
 
3.5%
U 14430
 
3.3%
5 11495
 
2.6%
Other values (28) 119758
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 434362
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 86343
19.9%
1 68426
15.8%
_ 43189
 
9.9%
2 25854
 
6.0%
A 17316
 
4.0%
4 16359
 
3.8%
3 15943
 
3.7%
S 15249
 
3.5%
U 14430
 
3.3%
5 11495
 
2.6%
Other values (28) 119758
27.6%

level2Name
Text

Missing 

Distinct1707
Distinct (%)4.0%
Missing412026
Missing (%)90.5%
Memory size3.5 MiB
2025-01-02T17:09:33.858450image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length28
Mean length8.320659473
Min length3

Characters and Unicode

Total characters359336
Distinct characters88
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique409 ?
Unique (%)0.9%

Sample

1st rown.a.
2nd rowSan Jose
3rd rowNanuque
4th rowPinogana
5th rowSintang
ValueCountFrequency (%)
city 2133
 
3.8%
rotuma 1089
 
1.9%
kabalebo 722
 
1.3%
san 695
 
1.2%
lau 640
 
1.1%
n.a 549
 
1.0%
sikaiana 534
 
1.0%
tengah 518
 
0.9%
antonio 476
 
0.8%
ambon 469
 
0.8%
Other values (1915) 48185
86.0%
2025-01-02T17:09:34.055293image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 53679
 
14.9%
n 26896
 
7.5%
o 25683
 
7.1%
e 21825
 
6.1%
i 21332
 
5.9%
u 16906
 
4.7%
r 16255
 
4.5%
t 15400
 
4.3%
l 14249
 
4.0%
12824
 
3.6%
Other values (78) 134287
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 359336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 53679
 
14.9%
n 26896
 
7.5%
o 25683
 
7.1%
e 21825
 
6.1%
i 21332
 
5.9%
u 16906
 
4.7%
r 16255
 
4.5%
t 15400
 
4.3%
l 14249
 
4.0%
12824
 
3.6%
Other values (78) 134287
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 359336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 53679
 
14.9%
n 26896
 
7.5%
o 25683
 
7.1%
e 21825
 
6.1%
i 21332
 
5.9%
u 16906
 
4.7%
r 16255
 
4.5%
t 15400
 
4.3%
l 14249
 
4.0%
12824
 
3.6%
Other values (78) 134287
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 359336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 53679
 
14.9%
n 26896
 
7.5%
o 25683
 
7.1%
e 21825
 
6.1%
i 21332
 
5.9%
u 16906
 
4.7%
r 16255
 
4.5%
t 15400
 
4.3%
l 14249
 
4.0%
12824
 
3.6%
Other values (78) 134287
37.4%

level3Gid
Text

Missing 

Distinct763
Distinct (%)5.5%
Missing441377
Missing (%)97.0%
Memory size3.5 MiB
2025-01-02T17:09:34.203264image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length14
Mean length12.36754608
Min length11

Characters and Unicode

Total characters171105
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique214 ?
Unique (%)1.5%

Sample

1st rowPHL.52.17.11_1
2nd rowPAN.5.2.4_1
3rd rowIDN.12.14.12_1
4th rowPHL.69.7.31_1
5th rowCMR.9.6.8_1
ValueCountFrequency (%)
idn.28.2.4_1 443
 
3.2%
bol.3.8.2_2 442
 
3.2%
per.18.3.4_1 329
 
2.4%
per.17.4.4_1 312
 
2.3%
idn.19.1.3_1 266
 
1.9%
cmr.9.6.8_1 253
 
1.8%
cmr.9.4.2_1 216
 
1.6%
phl.36.37.65_1 201
 
1.5%
phl.52.25.3_1 191
 
1.4%
phl.52.17.11_1 187
 
1.4%
Other values (753) 10995
79.5%
2025-01-02T17:09:34.420061image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 41505
24.3%
1 27915
16.3%
_ 13835
 
8.1%
2 11592
 
6.8%
P 7360
 
4.3%
5 6289
 
3.7%
L 6013
 
3.5%
H 5824
 
3.4%
3 5449
 
3.2%
4 5221
 
3.1%
Other values (24) 40102
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 171105
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 41505
24.3%
1 27915
16.3%
_ 13835
 
8.1%
2 11592
 
6.8%
P 7360
 
4.3%
5 6289
 
3.7%
L 6013
 
3.5%
H 5824
 
3.4%
3 5449
 
3.2%
4 5221
 
3.1%
Other values (24) 40102
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 171105
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 41505
24.3%
1 27915
16.3%
_ 13835
 
8.1%
2 11592
 
6.8%
P 7360
 
4.3%
5 6289
 
3.7%
L 6013
 
3.5%
H 5824
 
3.4%
3 5449
 
3.2%
4 5221
 
3.1%
Other values (24) 40102
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 171105
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 41505
24.3%
1 27915
16.3%
_ 13835
 
8.1%
2 11592
 
6.8%
P 7360
 
4.3%
5 6289
 
3.7%
L 6013
 
3.5%
H 5824
 
3.4%
3 5449
 
3.2%
4 5221
 
3.1%
Other values (24) 40102
23.4%

level3Name
Text

Missing 

Distinct731
Distinct (%)5.3%
Missing441442
Missing (%)97.0%
Memory size3.5 MiB
2025-01-02T17:09:34.567210image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length25
Mean length9.582207698
Min length3

Characters and Unicode

Total characters131947
Distinct characters91
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique211 ?
Unique (%)1.5%

Sample

1st rowSeñora Ascion
2nd rowMetetí
3rd rowSintang
4th rowPinontingan
5th rowMundemba
ValueCountFrequency (%)
santa 988
 
4.6%
poblacion 541
 
2.5%
ana 515
 
2.4%
timur 501
 
2.4%
kabaena 443
 
2.1%
san 351
 
1.7%
tambopata 329
 
1.5%
iquitos 312
 
1.5%
de 304
 
1.4%
barangay 304
 
1.4%
Other values (882) 16663
78.4%
2025-01-02T17:09:34.783238image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 22534
17.1%
n 11149
 
8.4%
o 9569
 
7.3%
7481
 
5.7%
i 6726
 
5.1%
u 6243
 
4.7%
r 5659
 
4.3%
e 5013
 
3.8%
t 4320
 
3.3%
l 3979
 
3.0%
Other values (81) 49274
37.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 131947
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 22534
17.1%
n 11149
 
8.4%
o 9569
 
7.3%
7481
 
5.7%
i 6726
 
5.1%
u 6243
 
4.7%
r 5659
 
4.3%
e 5013
 
3.8%
t 4320
 
3.3%
l 3979
 
3.0%
Other values (81) 49274
37.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 131947
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 22534
17.1%
n 11149
 
8.4%
o 9569
 
7.3%
7481
 
5.7%
i 6726
 
5.1%
u 6243
 
4.7%
r 5659
 
4.3%
e 5013
 
3.8%
t 4320
 
3.3%
l 3979
 
3.0%
Other values (81) 49274
37.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 131947
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 22534
17.1%
n 11149
 
8.4%
o 9569
 
7.3%
7481
 
5.7%
i 6726
 
5.1%
u 6243
 
4.7%
r 5659
 
4.3%
e 5013
 
3.8%
t 4320
 
3.3%
l 3979
 
3.0%
Other values (81) 49274
37.3%

iucnRedListCategory
Text

Missing 

Distinct9
Distinct (%)< 0.1%
Missing11501
Missing (%)2.5%
Memory size3.5 MiB
2025-01-02T17:09:34.836661image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters887422
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLC
2nd rowNE
3rd rowLC
4th rowLC
5th rowLC
ValueCountFrequency (%)
lc 278407
62.7%
ne 139325
31.4%
dd 10088
 
2.3%
vu 7110
 
1.6%
nt 4625
 
1.0%
en 2883
 
0.6%
cr 1136
 
0.3%
ex 119
 
< 0.1%
ew 18
 
< 0.1%
2025-01-02T17:09:34.935142image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 279543
31.5%
L 278407
31.4%
N 146833
16.5%
E 142345
16.0%
D 20176
 
2.3%
V 7110
 
0.8%
U 7110
 
0.8%
T 4625
 
0.5%
R 1136
 
0.1%
X 119
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 887422
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 279543
31.5%
L 278407
31.4%
N 146833
16.5%
E 142345
16.0%
D 20176
 
2.3%
V 7110
 
0.8%
U 7110
 
0.8%
T 4625
 
0.5%
R 1136
 
0.1%
X 119
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 887422
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 279543
31.5%
L 278407
31.4%
N 146833
16.5%
E 142345
16.0%
D 20176
 
2.3%
V 7110
 
0.8%
U 7110
 
0.8%
T 4625
 
0.5%
R 1136
 
0.1%
X 119
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 887422
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 279543
31.5%
L 278407
31.4%
N 146833
16.5%
E 142345
16.0%
D 20176
 
2.3%
V 7110
 
0.8%
U 7110
 
0.8%
T 4625
 
0.5%
R 1136
 
0.1%
X 119
 
< 0.1%